Evaluation of the relationship between midpalatal suture maturation and chronologic age with cone-beam computerised tomography via fractal analysis

Evaluation of the relationship between midpalatal suture maturation and chronologic age with cone-beam computerised tomography via fractal analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluation of the relationship between midpalatal suture maturation and chronologic age with cone-beam computerised tomography via fractal analysis Gulcan Kocal, Koray Halıcıoglu, Sıddıka Halıcıoglu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4184630/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The relationship between midpalatal suture maturation and chronological age was evaluated via cone-beam computed tomography (CBCT) via fractal analysis. Methods Cone-beam computed tomography images of 515 subjects with a mean age of 16 ± 3.6 years were included in the study. Midpalatal suture maturation was evaluated based on the classification described by Angelieri et al. In the second stage, the evaluation was conducted through quantitative data obtained by fractal analysis. Results There was a statistically significant difference between the fractal dimension and chronologic age related to the maturation of the midpalatal suture. There was a weak positive statistically significant correlation between the maturation of the midpalatal suture and the fractal dimension, but there was no statistically significant correlation between the maturation stage and the chronological age of the subjects. Conclusions Fractal analysis can be used to determine the maturation stages of the midpalatal suture. Considering the positive correlation between the fractal dimension and maturation of the midpalatal suture, the optimal fractal dimension cut-off value can be used to assess suture fusion. Chronologic age is not a precise indicator for evaluating the maturation of the midpalatal suture, but it can offer alternative guidance regarding suture fusion. Midpalatal suture CBCT Fractal analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Background The initiation and progression of fusion of the midpalatal suture varies according to sex and age. 1 The ossification process starts with bone spicules arising from suture margins, forming islands 2-4 along and within the suture, which increase with age. 3 , 5 Fusion occurs earlier in the posterior area of the suture, and the progression of ossification occurs from posterior to anterior 3 , 6 , as noted by the resorption of cortical bone and the formation of cancellous bone within the suture. 7 , 8 Some histological studies 2 , 3 , 5 , 6 , 9-12 and investigations involving microcomputerised tomography (micro-CT) 4 and occlusal radiographs 10 , 12 using human-palate specimens have been performed to evaluate suture morphology and maturation sequence. In addition, histological evaluation and tomographic studies 13 , 14 using animal samples have also been reported. Furthermore, there have been several clinical investigations using occlusal radiographs 15 , 16 and tomography. 1 , 17-28 However, occlusal radiographs are not reliable for analysing suture morphology because the midpalatal area is affected by the vomer and the structures of the external nose; therefore, incorrect radiological interpretation may result. 10 Over the last 15 years, cone-beam computed tomography (CBCT) has been used as a radiographic technique for diagnosis and treatment planning in orthodontics, especially for evaluating the relationship between the anatomy of the maxillofacial skeleton and surrounding structures. 29 The most important advantages of CBCT are the possibility of scanning the images in the sagittal, axial and coronal planes; reconstruction without magnification; obtaining three-dimensional images; and modelling using these images with software. 30 The radiation dose and cost of CBCT are lower than those of computed tomography (CT). 31 It is possible to identify the stages of fusion in the midpalatal suture by stating all the anteroposterior features of the suture without the other anatomical structures via CBCT. 1 Angelieri et al. 1 reported that midpalatal suture maturation can be classified into five stages by observing CBCT images (Table 1). The distributions of maturation stages and chronological ages reported in several studies 1 , 19 , 23 , 27 , 28 related to the midpalatal suture using the five-phase classification are presented in Table 2. Clinical studies 1 , 18 , 19 , 22-28 , 32 on the maturation of the midpalatal suture via CT and CBCT have been carried out in different populations, and the data are summarised in Table 2. The interpretability of radiographic images may vary between observers when selecting a region of interest, and it is important that the quality of the image is considered. 22 Therefore, a more objective CBCT-based method is required to determine the timing and sequence of fusion of the midpalatal suture. 33 Studies of the shape of human cranial sutures indicate that a suture can be confidentally seen as a fractal model. 34, 35 Fractal analysis is a statistical structure analysis that is derived from the mathematics of fractals to be able to identify complex shapes and structural textures. 36 The most important feature of fractal geometry is “self-similarity” (Fig. 1). In a self-similar object, parts forming the object resemble the whole structure. The patterns or irregular details are repeated on increasingly larger scales. When each part of each piece is enlarged, it resembles the entire object. 37 Therefore, fractal analysis is based on the fractal dimension (FD) 38 , and the FD is usually a fractional value. 39 The trabecular structure of the alveolar bone can be characterised by fractal analysis because it resembles itself when it is displayed at a certain resolution in radiographs. 40 A low FD indicates that the bone has a more porous structure comprising bony cavities and spaces. A high FD indicates that the architecture of the bone is more complex, more dense, and has fewer voids. 41 , 42 There are many methods for calculating the FD, each of which is based on its own theoretical basis. 38 The box counting method (Fig. 2) described by Russell et al. 43 is often applied and is the most suitable for performing fractal analysis. The box-counting algorithm is frequently used to measure the trabecular bone and bone marrow areas and quantify the trabecular structure. 44 , 45 The aim of the present study was to evaluate the relationship between midpalatal suture maturation and chronological age using CBCT and to investigate the maturation stages of the midpalatal suture via fractal analysis. In addition, the data were assessed to determine correlations and to determine the effectiveness of using fractal analysis as a criterion to determine suture maturation in clinical practice. Methods CBCT images of patients who underwent diagnosis (impacted tooth, skeletal malocclusion, etc.) and management at the archive of the Department of Oral and Maxillofacial Radiology, Abant Izzet Baysal University, Faculty of Dentistry, were used in this retrospective study. Since informed consent forms (permitting the use of data for academic research) were routinely obtained from all patients before taking CBCT scans, there was no need to contact patients again or request consent forms. The study was approved by the Institutional Clinical Research and Ethics Committee of Abant Izzet Baysal University. The sample size needed to reach statistical significance and a power analysis indicated that a minimum of 201 subjects would be needed for a statistical power of 90% at a significance level of 0.005. Of the 585 identified subjects, 70 were excluded because of a history of previous orthodontic treatment, lesions, incisive canal cysts, impacted teeth, sinus pneumatization, congenital bone defects or cleft palate in the midpalatal area. If the midpalatal suture was not included in the field of view, and when it was difficult to distinguish whether the suture was present due to poor-quality images, then they were not evaluated (Table 3). CBCT scans from 515 patients (315 females, 200 males) aged 6 to 26 years (female subjects, 16.1 ± 3.6 years; male subjects, 15.7 ± 3.7 years) were examined by a qualified radiologist (Table 4). While undergoing CBCT, the patients were sitting, in the natural head position and with the teeth in the maximum intercuspal position. CBCT (i-CAT: Imaging Sciences International, Hatfield, Pa, USA) images were taken using the following parameters: 120 kVp, 5 mA, 0.3 mm voxel size, and field-of-view, 16 X 7 or 16 X 13. Imaging software (i-CAT Vision) was used to evaluate the images. The selection of the evaluating slice was performed according to the protocol described by Angelieri et al. 1 The cursor of the software was positioned at the midsagittal plane of the patient on the axial plane, and the horizontal reference line was positioned at the centre of the palate in the maxillary first molar area on the coronal plane. A line passing from the anterior nasal spine to the posterior nasal spine was created by crossing the midpalatal suture in the sagittal plane. In this way, an image of the most central cross-sectional axial slice in the superior dimension (from the nasal level to the oral level of the palate) was obtained to determine the suture morphology and developmental stage (Fig. 3). The palate was evaluated in 2 central cross-sectional axial slices for subjects whose palate was curved, and the anterior and posterior regions were identified separately. The palate was evaluated in two of the most central axial slices in subjects whose palate was thicker, of which the more mature central cross-sectional axial slice was considered. Midpalatal suture maturation was first evaluated based on the classification system described by Angelieri et al. 1 (Fig. 4). Subsequently, midpalatal suture maturation was evaluated through quantitative data obtained by fractal analysis. A region of interest (ROI) was selected from the main image obtained on the axial plane for evaluation of the midpalatal suture via CBCT. A narrow ROI was established from the incisive canal to the posterior nasal spine by cropping the image so that the radiopaque region that may affect the calculation of FD density was excluded. The ImageJ v1.48 program, a version of NIH Image software, was used for FD calculations as specified by Arsan 46 and Kwak et al. 22 ROIs of the scheduled size on the digital images were converted into high-resolution “tiff” format (Fig. 5). ROIs were then processed based on the method developed by White and Rudolph, 47 in general, using the box counting method. The operations were performed by applying the menu and subfunction of the ImageJ program. The ROI was duplicated for image processing, and Gaussian blur was used to remove brightness variations due to the overlying soft tissues and varying thickness of the bone. The resulting image was then subtracted from the original image, and a 128 grayscale value was added to each pixel location. After the ROI was binarized, the bone marrow spaces and trabeculae were outlined. The noise of the resulting image was eliminated with erosion, and the outlines of the structures were emphasised using dilation. The image was inverted to make the trabeculae black and bone marrow spaces white. After skeletonisation, the image was divided into fragments with dimensions of 2-64 pixels with the “Fractal Box Count” option in the “Analyse” menu. The number of squares in which the trabeculae were located and the total number of frames in the image were calculated for each pixel of different sizes. The number of counted tiles was then plotted against the size of the box on a double logarithmic scale. The slope of the line fitted to the data points finally represented the FD (Fig. 6). The evaluation of the maturation stages of the midpalatal suture was carried out in a dark room. One hundred images were randomly selected after an interval of one month and evaluated again by the same observer to validate intraexaminer reliability. Moreover, the FD value was calculated by selecting the ROI again for the same images, and the exactness of the fractal analysis was considered. Statistical analysis The statistical analysis software used was MedCalc (version 12.7.7; MedCalc Software bvba, Ostend, Belgium), and p < 0.05 was considered to indicate statistical significance. The weighted kappa coefficient was calculated to evaluate intraexaminer reliability for the evaluation of the maturation stages of the midpalatal suture, and the Cronbach alpha coefficient was calculated to evaluate the FD for the first and second measurements. The Shapiro-Wilks test was used for testing normality. The Kruskal‒Wallis test was performed to compare fractal dimension and chronologic age at each maturation stage. The Mann‒Whitney U test was used for post hoc evaluation, and p < 0.0083 was considered to indicate statistical significance. The correlations between maturation stage and fractal dimension and between maturation stage and chronologic age were estimated using Spearman’s correlation coefficient. The optimal cut-off value of the fractal dimension was estimated by using a receiver operating characteristic (ROC) curve. Results The weighted kappa coefficient and the Cronbach’s alpha coefficient were 0.886 and 0.690, respectively. These results indicated good intraexaminer reliability. The number of patients with stage A disease was low ( n=4 ); therefore, this group was not included for statistical evaluation. The distribution of the maturation stages of the midpalatal suture according to chronological age and FD was variable, and there was a statistically significant difference between FD and chronological age with respect to the maturation stage of the midpalatal suture (Tables 5 and 6, respectively). As shown in Table 7, Stages B-E and C-E in males and Stages B-D, B-E and C-E in females were significantly different ( p <0.0083) between FD and maturation of the midpalatal suture. Stages C-E in males and C-D in females were significantly different ( p <0.0083) between chronological age and maturation of the midpalatal suture (Table 8). Although there was no statistically significant difference between FD and sex according to suture maturation, there was a statistically significant difference between chronological age and sex in stage E. Fusion of the palatine and maxillary regions of the midpalatal suture was completed at 16.2 ± 3.4 years for females and 18.7 ± 4 years for males, as presented in Table 6. There was a weak positive statistically significant correlation between the maturation stages of the midpalatal suture and FD in male and female subjects ( p < 0.001; Table 9, Fig. 7). The correlation coefficients for females and males were 0.230 and 0.205, respectively. However, there was no statistically significant correlation between maturation stage and chronological age in male or female subjects ( p > 0.05; Table 10). A ROC curve was used to express the boundary between maturation stages A-C and D or E, for which the midpalatal suture could be considered an FD. Fractal analysis was found to be a statistically significant method for predicting dichotomous maturation stage results for females and males. The optimal FD cut-off value was 0.942 for females and 0.948 for males (Fig. 8 and 9, respectively). Discussion In 1860, Angell 48 suggested that the maxilla could be expanded by opening the midpalatal suture. Halicioglu et al. 49 reported that orthopaedic loads generated during rapid maxillary expansion (RME) caused displacement of the bones adjacent to the maxilla, and if the structures forming the maxillary complex were unable to tolerate the force, relapse resulted through untipping of the anchorage teeth. Clinically, it has been reported that RME is indicated for patients who are still growing, and it is common that expansion treatment is unsuccessful in adults because of the fusion of the suture. 20 Gill et al. 50 specified that different treatment modalities, such as surgically assisted rapid maxillary expansion (SARME), should be used in cases in which the structure of the suture is complex, especially in adults. However, circumaxillary sutures are also important because of their resistance to expansion forces. The criterion for choosing conventional RME or SARME is usually chronological age. However, there is no SARME age consensus indicated in the literature. 20 Studies 10 , 19 , 51 , 52 on the morphology and maturation stage of the midpalatal suture and the time at which fusion occurs have revealed that the time and progress of fusion are quite varied. Melsen 9 reported that the morphology of the midpalatal suture changed at every stage of development and that it progressed with age, along with improvements in skeletal maturation and as the density of bone around the suture increased. 53 Several clinical investigations have reported that midpalatal suture fusion occurs via the use of occlusal radiographs 15 , 16 and tomography. 1 , 17-28 However, occlusal radiographs are not reliable for analysing the morphology of the midpalatal suture because the midpalatal area is overlain by the vomer and the structures of the external nose, so there may be incorrect radiological interpretation. 10 CT and CBCT are alternative methods that can provide three-dimensional and high-resolution images of craniofacial structures. 31 Korbmacher et al. 4 evaluated the maturation of the midpalatal suture of human specimens using micro-CT. However, the clinical evaluation of the midpalatal suture using micro-CT is not practical. 54 Franchi et al. 18 performed measurements of the maxillary bone around the midpalatal suture in Hounsfield units using a low dose of CT after RME expansion and at the end of the retention period. The density of the maxillary bone and the midpalatal suture was measured in Hounsfield units by Acar 32 on CT images taken before and after RME, and the correlation between the amount of dental and skeletal expansion and the measurement of bone density was evaluated. Using CBCT, a scan is completed by a single rotation in a short period of time, the image artifact is reduced by rapid scanning, and reconstruction without magnification is carried out. 30 At the same time, it is possible to visualise the midpalatal suture in vivo by avoiding the superposition of anatomical structures. 55 However, qualitative and quantitative analysis of the midpalatal suture can be facilitated using CBCT. 25 In the present study, we aimed to evaluate the relationship between chronological age and the maturation stage of the midpalatal suture and to obtain parameters that may provide concrete data about the stages of maturation. CBCT images of 515 patients (315 females, 200 males) aged 6 to 26 years were used for our investigation (Table 4). The average age of the participants in the sample group was 16 ± 3.6 years (female subjects 16.1 ± 3.6 years and male subjects 15.7 ± 3.7 years). The recommended age for expansion using SARME compared to RME varies 56-58 , and Alpern and Yurosko 59 suggested that SARME should be considered for males over the age of 25 and females over the age of 20. It was therefore decided that the age of the individuals should not be older than 26 years in the present study. In addition, there are ethical concerns regarding the exposure of subjects to unnecessary radiation. 23 According to the power analysis, the number of patients required should be as high as possible, so the present study included the greatest number of patients in the literature thus far 1 , 19 , 22-28 (Table 2). CBCT images were first examined using conventional methods to evaluate the maturation of the midpalatal suture. 1 , 22 However, conventional methods have limitations related to the likelihood that structures will look different depending on the position of the slice, which may cause misinterpretation. 23 Midpalatal suture maturation was first evaluated based on a five-stage classification system described by Angelieri et al. 1 and subsequently applied in many studies 19 , 20 , 22-28 that evaluated the maturation of the midpalatal suture (Table 1). Haghanifar et al. 26 modified this classification and, in addition to the described steps, noted that the anterior segment of the suture (in front of the nasopalatine foramen) was similar to stage C and that the posterior segment resembled stage D. This new form arose between stages C and D and is called stage CD. Stage D occurs after stage C. In stage CD, Haghanifar et al. 26 mentioned is accepted as stage D as defined by Angelieri et al. 1 in the present study because of fusion in the palatinal bone. In the second phase, fractal analysis, which allows objective clinical assessment of midpalatal suture maturation, was performed. A narrow box from the rear of the incisive canal to the posterior nasal spine was created as the ROI, following the recommendation of Kwak et al. 22 However, the radiopaque region that could affect FD calculation was excluded as much as possible. Previous studies 60 have indicated that the FD value is not affected by irradiation parameters, the angle of X-ray projection or the selection of the ROI. ROI selection was performed again on a random group of 100 CBCT images, and fractal analysis was conducted to evaluate intraexaminer reliability between the measurements. The Cronbach’s alpha coefficient was 0.690, and there was good intraexaminer reliability between the first and second measurements. In the present study, the box-counting method was also applied to investigate the midpalatal suture using fractal analysis, and processing of the ROIs was generally based on the method developed by White and Rudolph 47 . However, Geraets and van der Stelt 61 reported that FD may be different according to the method used in their study on bone disease, which was assessed using fractal analysis. In addition, it was also stated that errors related to the selection of ROIs and the methods used to process the images may affect the FD value. 22 Kwak et al. 22 reported that a more stable method of calculating FD for clinical use should be established and that the accuracy of the method may be improved by minimising the number of required calculation steps. According to previous studies 1 , 19 , 23 , 26-28 of the midpalatal suture, there is a difference between the periods in which the maturational stages are observed in females and males. In the present study, there was no fusion of the midpalatal suture in individuals younger than 11 years, except for a boy aged 10 years, and these results are similar to those of Angelieri et al. 1 who assessed a Brazilian population. Stage D was observed in females aged 17.6 ± 3.9 years and males aged 16.6 ± 3.6 years. The female and male subjects had stage D disease at least 11.2 and 12.1 years, respectively. Stage E was observed in females aged 16.2 ± 3.4 years and males aged 18.7 ± 4.06 years. The female and male subjects had stage E disease at least 11.3 and 10.1 years, respectively. According to the results of Melsen's research, 9 transverse growth of the midpalatal suture continues in females aged 16 years and in males aged 18, and the findings of the present study support these findings. Previous studies 1 , 9 , 19 , 23 have indicated that the time at which the midpalatal suture is fused is inconsistent. It has been reported 26 in an Iranian population that stage D, when fusion starts, was observed only in individuals over 40 years of age, and stage E was mostly observed in individuals over 50 years of age. In a study 27 involving adult Brazilian individuals, stages D and E were observed in subjects with a mean age of 32.3 ± 14.2 years and 38.7 ± 15.4 years, respectively. Tonello et al. 28 reported that stage D was more common in 14- and 15-year-olds, and stage E was more prevalent in those aged 14 and 15 years, except for a girl aged 12 years. Although there was a statistically significant relationship between chronologic age and maturation of the midpalatal suture in the present study, there was no correlation according to S pearman’s correlation coefficient (Table 10). It is therefore considered that chronological age is not reliable for determining the maturation of the midpalatal suture. Korbmacher et al. 4 reported that the level of interdigitation and the time of midpalatal fusion were independent of chronological age. However, Haghanifar et al. 26 reported that there was a strong correlation between age group and midpalatal suture maturation stage, that the level of ossification increased with age, and that the duration and extent of ossification and morphology varied widely among the different age groups. However, Haghanifar et al. 26 reported that chronologic age was not a reliable factor for determining the maturation stage of the midpalatal suture and that maturation should be determined using CBCT in all patients. Angelieri et al. 19 reported that chronological age may be a viable alternative for predicting some midpalatal suture stages (particularly the early stages), and Grünheid et al. 25 reported that chronologic age cannot be considered a useful parameter for predicting the maturation of the suture. When chronological age and sex were compared, the only statistically significant difference was found between female and male individuals at stage E. In contrast to the present findings, Nguyen et al. 17 and Haghanifar et al. 26 reported that there was no relationship between the fusion of the midpalatal suture and sex. This contradiction is likely the result of differences in sample size, study population, and methods used for assessing the midpalatal suture. According to the results of the present study, the relationship between FD and the maturation stage of the midpalatal suture was found to be statistically significant for females and males, and there was a weak positive statistically significant correlation between midpalatal suture maturation and FD in male and female subjects (Table 9 and Fig. 7). These results contradict the results of Kwak et al. 22 reported that there was a strong negative correlation between FD and maturation stage. These contrary results are due to differences in the methods used during the processing of the ROIs. In the present study, the analysis was performed by the box counting method based on the system developed by White and Rudolph 47 , and the " Invert " option was used, which varies from the methods of Kwak et al. 22 and Arsan 46 , in which the main lines of the trabecular bone were revealed. A histological study by Melsen, 9 in which suture morphology was evaluated, reported that at birth, the suture was broad and slightly sinuous, after which it later developed into a typical squamous suture, of which the palatine part covered the maxillary part. During puberty, the course of the suture was again slightly sinuous. Angelieri et al. 1 classified the maturation stages of the midpalatal suture compared to the histological morphology of the suture 2 , 8 , 9 and defined the suture as almost a straight high-density osseous line that appears as a scalloped irregular shape. If the FD is high, then the architecture of the bone is more complex and dense; 41 , 42 consequently, it is expected that FD and the maturation stage are positively correlated because the structure becomes more complex as maturation progresses. Fractal analysis was found to be a statistically significant method for predicting dichotomous maturation stage results for females and males in this study. The optimal FD cut-off value was 0.942 for females (Fig. 8) and 0.948 for males (Fig. 9). Kwak et al. 22 reported that the optimal FD cut-off value was 1.0235 for all subjects in a study that included 131 patients. Kang et al. 24 ROC curves were used to determine the cut-off values for the identification of pubertal growth spurts in females and males, and the optimal FD cut-off values for the midpalatal suture during pubertal growth were 0.9484 in males and 1.1205 in females. There appears to be a narrow range between 0.925 and 1.004 in the distribution of FD of the midpalatal suture in the present study. Therefore, investigations involving a greater sample size and age range may provide more reliable data. It should be noted that Isfeld et al. 54 assessed the different methods that were used to evaluate midpalatal maturation and stated that all new methods needed to be validated by histological references and further indicated that the use of multiple diagnostic criteria is extremely important for clinicians to accurately determine the maturation stage of the midpalatal suture. However, the use of CBCT and the choice of scanning protocol rely on good practice related to the image quality needed for the diagnostic task and the level of radiation exposure to the patient. 62 It is considered that it would have been more accurate to perform CBCT only on the maxilla; however, CBCT could not be performed due to the retrospective nature of the present study. Conclusions The results of the present study support the concept that using CBCT images to determine the morphology and degree of ossification of the midpalatal suture is valid. Although there was no significant correlation between midpalatal suture maturation and chronologic age, concrete findings were obtained that could assist clinicians in diagnosis and treatment planning when reaching the age and optimum FD cut-off value associated with suture fusion. Examination of midpalatal suture maturation with different parameters providing quantitative data such as measuring the density ratio of the suture using CBCT images and evaluating the relationships associated with skeletal maturation indices, which are frequently used for orthodontic diagnosis and treatment planning, can provide more beneficial results. Abbreviations Micro Computerised Tomography Micro CT Cone-beam computed tomography CBCT Computed tomography CT Fractal dimension FD Region of interest ROI Declarations Ethics approval and consent to participate CBCT images at the archive of the Department of Oral and Maxillofacial Radiology, Abant Izzet Baysal University, Faculty of Dentistry, were used in this retrospective study. Since informed consent forms (permitting the use of data for academic research) were routinely obtained from all patients before taking CBCT scans by the Department of Oral and Maxillofacial Radiology, Abant Izzet Baysal University, there was no need to contact patients again or request consent forms. The Institutional Clinical Research and Ethics Committee of Abant Izzet Baysal University approved the study protocol (reference number: 20116/85 and the date: 24.11.2016). Consent for publication Not Applicable. Data-availability The datasets used and/or analysed during the current study available from the corresponding author on reasonable reques. Competing interests The authors declare no competing interests. Funding The authors declared that this study has received no financial support. Author Contributions Concept – G.K., K.H.; Design – G.K., K.H., S.H.; Data Collection and/or Processing – G.K., S.H.; Analysis and/or Interpretation – G.K., S.H.; Literature Review G.K., K.H., S.H.; Writing – G.K., S.H; Critical Review – G.K., K.H., S.H. Acknowledgements Not applicable References Angelieri F, Cevidanes LH, Franchi L, Goncalves JR, Benavides E, McNamara JA. Midpalatal suture maturation: Classification method for individual assessment before rapid maxillary expansion. Am J Orthod Dentofacial Orthop 2013;144:759-69. Persson M, Magnusson BC, Thilander B. Sutural closure in rabbit and man: a morphological and histochemical study. J Anat 1978;125:313-21. Persson M, Thilander B. 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Uchiyama T, Tanizawa T, Muramatsu H, Endo N, Takahashi HE, Hara T. Three-dimensional microstructural analysis of human trabecular bone in relation to its mechanical properties. Bone 1999;25:487-91. Drummond JL, Thompson MT, Super BJ. Fracture surface examination of dental ceramics using fractal analysis. Dent Mater 2005;21:586-9. Arsan B, Köse TE, Çene E, Özcan İ. Assessment of the trabecular structure of mandibular condyles in patients with temporomandibular disorders using fractal analysis. Oral Surg Oral Med Oral Pathol Oral Radiol 2017;123:382-91. White SC, Rudolph DJ. Alterations of the trabecular pattern of the jaws in patients with osteoporosis. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 1999;88:628-35. Angell EH. Treatment of irregularity of the permanent or adult teeth. Dent Cosmos 1860;1:540-4. K H, Kiki A, Yavuz İ. Maxillary expansion with the memory screw: a preliminary investigation. Korean J Orthod 2012;42:73-9. Gill D, Naini F, McNally M, Jones A. The management of transverse maxillary deficiency. Dent Update 2004;31:516-23. Chrcanovic BR, Custódio ALN. Orthodontic or surgically assisted rapid maxillary expansion. Oral Maxillofac Surg 2009;13:123-37. Primožič J, Perinetti G, Richmond S, Ovsenik M. Three-dimensional longitudinal evaluation of palatal vault changes in growing subjects. Angle Orthod 2011;82:632-6. Salgueiro DG, Rodrigues VH, Tieghi Neto V, Menezes CC, Goncales ES, Ferreira Junior O. Evaluation of opening pattern and bone neoformation at median palatal suture area in patients submitted to surgically assisted rapid maxillary expansion (SARME) through cone beam computed tomography. J Appl Oral Sci 2015;23:397-404. Isfeld D, Lagravere M, Leon-Salazar V, Flores-Mir C. Novel methodologies and technologies to assess mid-palatal suture maturation: a systematic review. Head Face Med 2017;13:13. Liu S, Xu T, Zou W. Effects of rapid maxillary expansion on the midpalatal suture: a systematic review. Eur J Orthod 2015;37:651-5. Mommaerts MY. Transpalatal distraction as a method of maxillary expansion. Br J Oral Maxillofac Surg 1999;37:268-72. Mossaz CF, Byloff FK, Richter M. Unilateral and bilateral corticotomies for correction of maxillary transverse discrepancies. Eur J Orthod 1992;14:110-6. Timms DJ, Vero D. The relationship of rapid maxillary expansion to surgery with special reference to midpalatal synostosis. Br J Oral Surg 1981;19:180-96. Alpern MC, Yurosko JJ. Rapid palatal expansion in adults: with and without surgery. Angle Orthod 1987;57:245-63. Shrout MK, Potter NJ, Mailhot JM, Hildebolt CF. Morphologic operations used to distinguish between two patient populations differing in periodontal health. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 1998;85:334-8. Geraets WG, Van der Stelt PF. Fractal properties of bone. Dentomaxillofac Radiol 2000;29:144-53. Al-Okshi A, Lindh C, Sale H, Gunnarson M, Rohlin M. Effective dose of cone beam CT (CBCT) of the facial skeleton: a systematic review. Br J Radiol 2015; 88:20140658 Tables Table 1. The distribution of maturation stages and chronologic age of the studies related to the midpalatal suture. (n: Number of patients, sd: Standard deviation, Min: Minimum, Max: Maximum) Author Angelieri et al. 1 2013 Angelieri et al. 19 2015 Maturation Stage A B C D E A B C D E Female n 3 35 16 11 21 6 24 25 11 17 Chronologic age Mean ±sd - - - - - 6.2 ± 0.5 9 ± 2.1 14.8 ± 9.2 21.7 ± 10 20.1 ± 11.2 Min - - - - - 5.5 5.6 9.8 13.6 12.8 Max - - - - - 6.7 13.6 58.4 47.4 55.2 Male n 2 22 15 6 9 10 21 17 5 5 Chronologic age Mean ±sd - - - - - 6.5 ± 1.6 11.6 ± 1.7 14.4 ± 3.5 24.9 ± 9.6 32.7 ± 11.1 Min - - - - - 5.3 7.9 10.6 14.6 18.5 Max - - - - - 10.3 14.9 26.3 37.7 44.8 Table 1. The distribution of maturation stages and chronologic age of the studies related to the midpalatal suture. (n: Number of patients, sd: Standard deviation, Min: Minimum, Max: Maximum) (Continued) Author Jang et al. 23 2016 Angelieri et al. 27 2017 Tonello et al. 28 2017 Maturation Stage A B C D E A B C D E A B C D E Female n 6 4 20 13 16 0 3 4 15 42 1 9 25 6 3 Chronologic age Mean ±sd - - - - - - -- - - - - - - - - Min 6 7 8 10 11 - -- - - - 11 11 11 12 12 Max 9 11 14 17 18 - -- - - - 11 12 15 15 15 Male n 13 10 7 6 4 0 0 2 4 8 0 12 17 5 6 Chronologic age Mean ±sd - - - - - - - - - - - - - - - Min 8 8 9 13 15 - - - - - - 11 11 12 14 Max 11 13 14 19 20 - - - - - - 15 15 15 15 Table 2. The studies performed about the maturation of the midpalatal suture. (n: Number of patient, sd: Standard deviation, Min: Minimum, Max: Maximum) Chronologic age n Total Sample Female Male Author Year Method Population Total Female Male Mean ± sd Min Max Mean ± sd Min Max Mean ± sd Min Max Franchi et al. 18 2010 CT Italian 17 10 7 11.2 8 14 - - - - - - Acar 32 2013 CT Turkish 22 11 11 14.25 11 17 13.6 11 15 14.9 13.5 17 Angelieri et al. 1 2013 CBCT Brazilian 140 86 54 - 5.6 58.4 - - - - - - Angelieri et al. 19 2015 CBCT Brazilian 142 84 58 14.8 ± 9.7 5.3 58.4 - 5.5 55.2 - 5.3 44.8 Kwak et al. 22 2016 CBCT Korean 131 62 69 24.1 ± 5.9 18.1 53.4 25.2 ± 5.9 - - 23.1 ± 5.8 - - Jang et al. 23 2016 CBCT Korean 99 59 40 12.03 ± 3.221 6 20 13.56 ± 3.12 6 20 14.3 ± 3.27 8 18 Kang et al. 24 2016 CBCT Korean 165 84 81 15.5 ± 7.6 - - - - - - - - Grünheid et al. 25 2017 CBCT American 30 17 13 12.9 ± 2.1 - - - - - - - - Haghanifar et al. 26 2017 CBCT Iran 144 72 72 39.62 ±1 7.31 - - 39.83 ± 17.06 - - 39.42 ± 17.68 - - Angelieri ve ark. 27 20167 CBCT Brazilian 78 64 14 36.4 ± 15 18 66 - - - - - - Tonello ve ark. 28 2017 CBCT Brazilian 84 44 40 - 11 15 - 11 15 - 11 15 Table 3. The criterias of the radioragraphs that were not included for the study a history of previous orthodontic treatment congenital bone defects Lesions cleft palate incisive canal cyst the field of view not included the midpalatal suture impacted teeth the poor-quality images sinus pneumatization Table 4. Gender and demographics of the sample. (n: Number of patients, sd: Standard deviation, Min: Minimum, Max: Maximum ) n % Mean ± sd Median Min Max Female 315 61.2 16.1 ± 3.6 15.7 8.8 26 Male 200 38.8 15.7 ± 3.7 15.0 6 26 Total Sample 515 100 16.0 ± 3.6 15.3 6.0 26 Table 5. Distribution of the maturation stages and mean FD. (n: Number of patients, sd: Standard deviation, Min: Minimum, Max: Maximum) Female Male n Mean ± sd Median (Min-Max) n Mean ± sd Median (Min-Max) p A 0 - - 4 0.92 ± 0.02 0.91 (0.89-0.95) - B 151 0.93 ± 0.03 0.93 (0.82-0.99) 66 0.94 ± 0.06 0.94 (0.85-1.0) 0.339 C 107 0.94 ± 0.02 0.94 (0.83-0.99) 99 0.94 ± 0.03 0.94 (0.85-1.0) 0.349 D 40 0.95 ± 0.02 0.95 (0.89-1.0) 16 0.94 ± 0.02 0.94 (0.92-0.97) 0.848 E 17 0.95 ± 0.01 0.95 (0.93-0.98) 15 0.95 ± 0.02 0.96 (0.89-0.99) 0.411 Table 6. Distribution of the maturation stages and mean chronologic age. (n: Number of patients, sd: Standard deviation, Min: Minimum, Max: Maximum ) Female Male n Mean ± sd Medyan (Min-Max) n Mean ± sd Medyan (Min-Max) p A 0 - - 4 12.9 ± 1.03 13.4 (11.5-13.7) - B 151 16.2 ± 3.3 15.7 (8.8-24.6) 66 15.9 ± 3.8 15.2 (6.5-25) 0.410 C 107 15.4 ± 3.6 14.7 (9-26) 99 14.9 ± 3.41 14.7 (6-26) 0.692 D 40 17.6 ± 3.9 17.6 (11.2-25.3) 16 16.6 ± 3.6 16.2 (12.1-23.3) 0.394 E 17 16.2 ± 3.4 15.8 (11.3-23.8) 15 18.7 ± 4.06 18.5 (10.1-26) 0.049 Table 7. Binary comparison of the statistically significant relationship between FD and the maturarion stages of the midpalatal suture. B-C B-D B-E C-D C-E D-E Male p 0.315 0.181 <0.001 0.499 0.022 0.175 Female p 0.107 <0.001 <0.001 0.037 <0.001 0.226 Table 8. Binary comparison of the statistically significant relationship between chronologic age and the maturarion stages of the midpalatal suture. B-C B-D B-E C-D C-E D-E Male p 0.111 0.535 0.008 0.137 <0.001 0.151 Female p 0.021 0.032 0.937 <0.001 0.281 0.168 Table 9. Spearman’s correlation coeffcients for maturation stage and FD. Female Male Total Correlation cofficient 0.230 0.205 0.226 p <0.001 <0.001 <0.001 Table 10. Spearman’s correlation coeffcients for the maturation stage and chronologic age. Female Male Total Correlation cofficient 0.009 0.109 0.041 p 0.873 0.125 0.349 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4184630","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":287303781,"identity":"d8abab2a-6d68-4a7d-b636-991645bd1b2b","order_by":0,"name":"Gulcan 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natural fractal example, at different magnifications\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4184630/v1/6ffc70dbbdf39fa89a4da627.png"},{"id":54315714,"identity":"71abc588-1591-43e0-baea-066f01ed6b09","added_by":"auto","created_at":"2024-04-08 17:44:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67457,"visible":true,"origin":"","legend":"\u003cp\u003eCalculation of the FB value of a structure with fractal properties using the box counting method\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4184630/v1/f054bf691441be650f18a84a.png"},{"id":54315724,"identity":"a865915d-f7a4-49b8-b112-4f1b00dbef63","added_by":"auto","created_at":"2024-04-08 17:44:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":180371,"visible":true,"origin":"","legend":"\u003cp\u003eThe midpalatal suture on the axial, sagittal and coronal planes\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4184630/v1/84859cf70ad047300fb0b808.png"},{"id":54315719,"identity":"dddae8dd-5f29-49b2-bdaa-0f7070044e23","added_by":"auto","created_at":"2024-04-08 17:44:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":112183,"visible":true,"origin":"","legend":"\u003cp\u003eThe classification of the midpalatal suture that was described by Angelieri et al.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4184630/v1/6136fd06b5c84ef1e52eba2c.png"},{"id":54315715,"identity":"ae11e6c2-6172-4edc-be90-008816395f9e","added_by":"auto","created_at":"2024-04-08 17:44:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":127008,"visible":true,"origin":"","legend":"\u003cp\u003eROI selection\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4184630/v1/f6f98e0a92c1b38f74525a7e.png"},{"id":54316465,"identity":"973f615f-93f9-48fb-8bf2-2f483812367e","added_by":"auto","created_at":"2024-04-08 17:52:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":35274,"visible":true,"origin":"","legend":"\u003cp\u003eSlope of the line fitted to the data points along with the FD value\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4184630/v1/b98c752e5e3d54ad5e20e476.png"},{"id":54315722,"identity":"17d04ca4-0032-44c1-8329-a13927cd0b39","added_by":"auto","created_at":"2024-04-08 17:44:59","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":32119,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot depicting maturation stage and FD\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4184630/v1/c2ee41abdcbc2707e81c05c5.png"},{"id":54315723,"identity":"0e7de3ed-c730-48fe-962c-2d8803d00b1a","added_by":"auto","created_at":"2024-04-08 17:44:59","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":16285,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curve and optimal FD cut-off value in females\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4184630/v1/b8c33adf2bc47fb2ca1d7cc0.png"},{"id":54315720,"identity":"67b7309e-5f16-4431-89db-6e917979acc2","added_by":"auto","created_at":"2024-04-08 17:44:59","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":15947,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curve and optimal FD cut-off value in males\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-4184630/v1/92566230284247f4ac85255d.png"},{"id":77189312,"identity":"1016e48e-e11d-4262-a300-0b4fedd60dc6","added_by":"auto","created_at":"2025-02-26 04:46:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3380509,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4184630/v1/b4f0ea66-330b-4b09-8aca-fbd57d48fedb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of the relationship between midpalatal suture maturation and chronologic age with cone-beam computerised tomography via fractal analysis","fulltext":[{"header":"Background","content":"\u003cp\u003eThe initiation and progression of fusion of the midpalatal suture varies according to sex and age.\u003csup\u003e1\u003c/sup\u003e The ossification process starts with bone spicules arising from suture margins, forming islands\u003csup\u003e2-4\u003c/sup\u003e along and within the suture, which increase with age.\u003csup\u003e3\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e5\u003c/sup\u003e Fusion occurs earlier in the posterior area of the suture, and the progression of ossification occurs from posterior to anterior\u003csup\u003e3\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e6\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e as noted by the resorption of cortical bone and the formation of cancellous bone within the suture.\u003csup\u003e7\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e8\u003c/sup\u003e Some histological studies\u003csup\u003e2\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e3\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e5\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e6\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e9-12\u003c/sup\u003e and investigations involving microcomputerised tomography (micro-CT)\u003csup\u003e4\u003c/sup\u003e and occlusal radiographs\u003csup\u003e10\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e12\u003c/sup\u003e using human-palate specimens have been performed to evaluate suture morphology and maturation sequence. In addition, histological evaluation and tomographic studies\u003csup\u003e13\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e14\u003c/sup\u003e using animal samples have also been reported. Furthermore, there have been several clinical investigations using occlusal radiographs\u003csup\u003e15\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e16\u003c/sup\u003e and tomography.\u003csup\u003e1\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e17-28\u003c/sup\u003e However, occlusal radiographs are not reliable for analysing suture morphology because the midpalatal area is affected by the vomer and the structures of the external nose; therefore, incorrect radiological interpretation may result.\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eOver the last 15 years, cone-beam computed tomography (CBCT) has been used as a radiographic technique for diagnosis and treatment planning in orthodontics, especially for evaluating the relationship between the anatomy of the maxillofacial skeleton and surrounding structures.\u003csup\u003e29\u003c/sup\u003e The most important advantages of CBCT are the possibility of scanning the images in the sagittal, axial and coronal planes; reconstruction without magnification; obtaining three-dimensional images; and modelling using these images with software.\u003csup\u003e30\u003c/sup\u003e The radiation dose and cost of CBCT are lower than those of computed tomography (CT).\u003csup\u003e31\u003c/sup\u003e It is possible to identify the stages of fusion in the midpalatal suture by stating all the anteroposterior features of the suture without the other anatomical structures via CBCT.\u003csup\u003e1\u003c/sup\u003e Angelieri et al.\u003csup\u003e1\u003c/sup\u003e reported that midpalatal suture maturation can be classified into five stages by observing CBCT images (Table 1). The distributions of maturation stages and chronological ages reported in several studies\u003csup\u003e1\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e19\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e23\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e27\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e28\u003c/sup\u003e related to the midpalatal suture using the five-phase classification are presented in Table 2. Clinical studies\u003csup\u003e1\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e18\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e19\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e22-28\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e32\u003c/sup\u003e on the maturation of the midpalatal suture via CT and CBCT have been carried out in different populations, and the data are summarised in Table 2.\u003c/p\u003e\n\u003cp\u003eThe interpretability of radiographic images may vary between observers when selecting a region of interest, and it is important that the quality of the image is considered.\u003csup\u003e22\u003c/sup\u003e Therefore, a more objective CBCT-based method is required to determine the timing and sequence of fusion of the midpalatal suture.\u003csup\u003e33\u003c/sup\u003e Studies of the shape of human cranial sutures indicate that a suture can be confidentally seen as a fractal model.\u003csup\u003e34,\u003c/sup\u003e\u003csup\u003e35\u003c/sup\u003e Fractal analysis is a statistical structure analysis that is derived from the mathematics of fractals to be able to identify complex shapes and structural textures.\u003csup\u003e36\u003c/sup\u003e The most important feature of fractal geometry is \u0026ldquo;self-similarity\u0026rdquo; (Fig. 1). In a self-similar object, parts forming the object resemble the whole structure. The patterns or irregular details are repeated on increasingly larger scales. When each part of each piece is enlarged, it resembles the entire object.\u003csup\u003e37\u003c/sup\u003e Therefore, fractal analysis is based on the fractal dimension (FD)\u003csup\u003e38\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e and the FD is usually a fractional value.\u003csup\u003e39\u003c/sup\u003e The trabecular structure of the alveolar bone can be characterised by fractal analysis because it resembles itself when it is displayed at a certain resolution in radiographs.\u003csup\u003e40\u003c/sup\u003e A low FD indicates that the bone has a more porous structure comprising bony cavities and spaces. A high FD indicates that the architecture of the bone is more complex, more dense, and has fewer voids.\u003csup\u003e41\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e42\u003c/sup\u003e There are many methods for calculating the FD, each of which is based on its own theoretical basis.\u003csup\u003e38\u003c/sup\u003e The box counting method (Fig. 2) described by Russell et al.\u003csup\u003e43\u003c/sup\u003e is often applied and is the most suitable for performing fractal analysis. The box-counting algorithm is frequently used to measure the trabecular bone and bone marrow areas and quantify the trabecular structure.\u003csup\u003e44\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e45\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe aim of the present study was to evaluate the relationship between midpalatal suture maturation and chronological age using CBCT and to investigate the maturation stages of the midpalatal suture via fractal analysis. In addition, the data were assessed to determine correlations and to determine the effectiveness of using fractal analysis as a criterion to determine suture maturation in clinical practice.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eCBCT images of patients who underwent diagnosis (impacted tooth, skeletal malocclusion, etc.) and management\u0026nbsp;at\u0026nbsp;the archive of the Department of Oral and Maxillofacial Radiology, Abant Izzet Baysal University, Faculty of Dentistry,\u0026nbsp;were used in this retrospective study. Since informed consent forms (permitting the use of data for academic research) were routinely obtained from all patients before taking CBCT scans, there was no need to contact patients again or request consent forms. The study was approved by the Institutional Clinical Research and Ethics Committee of Abant Izzet Baysal University.\u0026nbsp;The sample size needed to reach statistical significance and a power analysis indicated that a minimum of 201 subjects would be needed for a statistical power of 90% at a\u0026nbsp;significance\u0026nbsp;level of 0.005. Of\u0026nbsp;the\u0026nbsp;585 identified subjects, 70 were excluded because of a history of previous orthodontic treatment, lesions, incisive canal cysts, impacted teeth, sinus\u0026nbsp;pneumatization, congenital bone defects or cleft palate in the midpalatal area. If the midpalatal suture was not included in the field of view, and when it was difficult to distinguish\u0026nbsp;whether\u0026nbsp;the suture was present due to poor-quality images, then they were not evaluated (Table 3). CBCT scans from 515 patients (315\u0026nbsp;females, 200\u0026nbsp;males) aged\u0026nbsp;6 to 26 years (female subjects,\u0026nbsp;16.1 \u0026plusmn; 3.6 years;\u0026nbsp;male subjects,\u0026nbsp;15.7 \u0026plusmn; 3.7\u0026nbsp;years) were examined by a qualified radiologist (Table 4).\u003c/p\u003e\n\u003cp\u003eWhile undergoing CBCT, the patients were sitting, in\u0026nbsp;the\u0026nbsp;natural head position and\u0026nbsp;with\u0026nbsp;the teeth in\u0026nbsp;the\u0026nbsp;maximum intercuspal\u0026nbsp;position. CBCT (i-CAT: Imaging Sciences International, Hatfield, Pa, USA) images were taken using the following parameters: 120 kVp,\u0026nbsp;5 mA, 0.3 mm voxel size, and field-of-view, 16 X 7 or 16 X 13.\u0026nbsp;Imaging\u0026nbsp;software (i-CAT Vision) was used to evaluate the images. The selection of the evaluating slice was\u0026nbsp;performed\u0026nbsp;according to the protocol described by Angelieri et al.\u003ca href=\"#_ENREF_1\" title=\"Angelieri F, 2013 #1\"\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/a\u003e The cursor of the software was positioned at the midsagittal plane of the patient on the axial plane, and the horizontal reference line was positioned at the centre of the palate in the maxillary first molar area on the coronal plane. A line passing from the anterior nasal spine to the posterior nasal spine was\u0026nbsp;created by\u0026nbsp;crossing the midpalatal suture in the sagittal plane. In this way, an image\u0026nbsp;of\u0026nbsp;the most central cross-sectional axial slice in the superior dimension (from the nasal\u0026nbsp;level\u0026nbsp;to the oral level of the palate) was obtained to determine the suture morphology and developmental stage (Fig. 3). The palate was evaluated in 2 central cross-sectional axial slices for subjects whose palate was curved, and the anterior and posterior regions were identified separately. The palate was evaluated in two of the most central axial slices in subjects whose palate was thicker, of which the more mature central cross-sectional axial slice\u0026nbsp;was\u0026nbsp;considered.\u003c/p\u003e\n\u003cp\u003eMidpalatal\u0026nbsp;suture maturation was\u0026nbsp;first\u0026nbsp;evaluated based on the classification\u0026nbsp;system\u0026nbsp;described by Angelieri et al.\u003ca href=\"#_ENREF_1\" title=\"Angelieri F, 2013 #1\"\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/a\u003e (Fig. 4). Subsequently, midpalatal suture maturation was\u0026nbsp;evaluated\u0026nbsp;through quantitative data obtained by fractal analysis. A region of interest (ROI) was selected from the main image obtained on the axial plane for evaluation of the midpalatal suture via CBCT. A narrow ROI was\u0026nbsp;established\u0026nbsp;from the incisive canal to the posterior nasal spine by cropping the image so that the radiopaque region that may affect\u0026nbsp;the\u0026nbsp;calculation of FD\u0026nbsp;density\u0026nbsp;was excluded.\u0026nbsp;The\u0026nbsp;ImageJ v1.48 program, a version of NIH Image software, was used\u0026nbsp;for FD calculations\u0026nbsp;as specified by Arsan\u003ca href=\"#_ENREF_46\" title=\"Arsan B, 2017 #72\"\u003e\u003csup\u003e46\u003c/sup\u003e\u003c/a\u003e and Kwak et al.\u003ca href=\"#_ENREF_22\" title=\"Kwak KH, 2016 #22\"\u003e\u003csup\u003e22\u003c/sup\u003e\u003c/a\u003e ROIs\u0026nbsp;of\u0026nbsp;the scheduled size on the digital images were converted into high-resolution \u0026ldquo;tiff\u0026rdquo; format (Fig. 5). ROIs were then processed based on the method developed by White and Rudolph,\u003ca href=\"#_ENREF_47\" title=\"White SC, 1999 #48\"\u003e\u003csup\u003e47\u003c/sup\u003e\u003c/a\u003e in general, using the box counting method. The operations were performed by applying the menu and subfunction of the\u0026nbsp;ImageJ\u0026nbsp;program. The ROI was duplicated for image processing,\u0026nbsp;and Gaussian blur was used to remove\u0026nbsp;brightness variations due to\u0026nbsp;the\u0026nbsp;overlying soft tissues and\u0026nbsp;varying thickness of\u0026nbsp;the\u0026nbsp;bone. The resulting\u0026nbsp;image was then subtracted from the original image,\u0026nbsp;and a\u0026nbsp;128\u0026nbsp;grayscale\u0026nbsp;value was added to each pixel location. After the ROI\u0026nbsp;was binarized, the\u0026nbsp;bone marrow spaces\u0026nbsp;and trabeculae were outlined. The noise\u0026nbsp;of the resulting image was eliminated with erosion,\u0026nbsp;and the outlines of the structures were emphasised using dilation. The image was inverted to make the trabeculae black and bone marrow spaces white.\u0026nbsp;After\u0026nbsp;skeletonisation, the image was divided into fragments with dimensions of 2-64 pixels with the \u0026ldquo;Fractal Box Count\u0026rdquo; option in the \u0026ldquo;Analyse\u0026rdquo; menu. The\u0026nbsp;number of\u0026nbsp;squares in which the trabeculae were located and the total number of frames in the image were calculated for each pixel\u0026nbsp;of\u0026nbsp;different\u0026nbsp;sizes. The number of counted tiles was then plotted against the size of the box on a double logarithmic scale. The slope of the line fitted to the data points finally represented the FD (Fig. 6).\u003c/p\u003e\n\u003cp\u003eThe evaluation of the\u0026nbsp;maturation\u0026nbsp;stages of the midpalatal suture was carried out in a dark room. One hundred images were randomly selected after an interval of one month and evaluated again by the same observer to validate intraexaminer reliability. Moreover, the FD value was calculated by selecting the ROI again for the same images,\u0026nbsp;and the exactness of the fractal analysis was considered.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eanalysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe statistical analysis software used was MedCalc (version 12.7.7; MedCalc Software bvba, Ostend, Belgium), and\u0026nbsp;\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05 was considered\u0026nbsp;to indicate statistical significance. The weighted kappa coefficient was calculated to evaluate intraexaminer reliability for the evaluation of the\u0026nbsp;maturation\u0026nbsp;stages of the midpalatal suture, and the Cronbach\u0026nbsp;alpha\u0026nbsp;coefficient was calculated to evaluate the FD for the first and second measurements.\u0026nbsp;The\u0026nbsp;Shapiro-Wilks test was used for testing normality. The Kruskal‒Wallis test was performed to compare fractal dimension and chronologic age at each maturation stage. The\u0026nbsp;Mann‒Whitney\u0026nbsp;U test was used for\u0026nbsp;post hoc\u0026nbsp;evaluation, and \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.0083 was considered to indicate statistical significance. The correlations between maturation stage and fractal dimension and between maturation stage and chronologic age were estimated using Spearman\u0026rsquo;s correlation coefficient. The optimal cut-off value of the fractal dimension was estimated by using a receiver operating characteristic (ROC) curve.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe weighted kappa coefficient and the\u0026nbsp;Cronbach\u0026rsquo;s alpha\u0026nbsp;coefficient were 0.886 and 0.690, respectively. These results indicated good intraexaminer reliability.\u003c/p\u003e\n\u003cp\u003eThe number of patients with\u0026nbsp;stage\u0026nbsp;A\u0026nbsp;disease\u0026nbsp;was low (\u003cem\u003en=4\u003c/em\u003e);\u0026nbsp;therefore, this group was not included for statistical evaluation. The distribution of the maturation stages of the midpalatal suture according to\u0026nbsp;chronological\u0026nbsp;age and FD was variable,\u0026nbsp;and there was a statistically significant difference between FD and\u0026nbsp;chronological\u0026nbsp;age with\u0026nbsp;respect to\u0026nbsp;the maturation\u0026nbsp;stage\u0026nbsp;of the midpalatal suture (Tables 5 and 6, respectively). As\u0026nbsp;shown\u0026nbsp;in Table 7,\u0026nbsp;Stages\u0026nbsp;B-E and C-E in males and\u0026nbsp;Stages\u0026nbsp;B-D, B-E and C-E in females were\u0026nbsp;significantly different\u0026nbsp;(\u003cem\u003ep\u003c/em\u003e \u0026lt;0.0083) between FD and maturation of the midpalatal suture.\u0026nbsp;Stages\u0026nbsp;C-E in males and C-D in females were\u0026nbsp;significantly different\u0026nbsp;(\u003cem\u003ep\u003c/em\u003e \u0026lt;0.0083) between\u0026nbsp;chronological\u0026nbsp;age and maturation of the midpalatal suture (Table 8).\u0026nbsp;Although\u0026nbsp;there was no statistically significant difference between FD and\u0026nbsp;sex\u0026nbsp;according to suture maturation, there was a statistically significant difference between\u0026nbsp;chronological\u0026nbsp;age and\u0026nbsp;sex\u0026nbsp;in stage E. Fusion of the palatine and maxillary regions of the midpalatal suture was completed at 16.2 \u0026plusmn; 3.4 years for females and 18.7 \u0026plusmn; 4 years for males, as presented in Table 6.\u003c/p\u003e\n\u003cp\u003eThere was a weak positive statistically significant correlation between the maturation stages of the midpalatal suture and FD in male and female subjects (\u003cem\u003ep \u003cstrong\u003e\u0026lt;\u0026nbsp;\u003c/strong\u003e\u003c/em\u003e0.001; Table 9, Fig. 7). The correlation coefficients for females and males were 0.230 and 0.205, respectively. However, there was no statistically significant correlation between maturation\u0026nbsp;stage\u0026nbsp;and\u0026nbsp;chronological\u0026nbsp;age in male or female subjects (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026gt;\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e0.05; Table 10).\u003c/p\u003e\n\u003cp\u003eA ROC curve was used to express the boundary between maturation stages A-C and D or E, for which the midpalatal suture could be considered an FD. Fractal analysis was found to be a statistically significant method for predicting dichotomous maturation stage results for females and males. The optimal FD cut-off value was 0.942 for females and 0.948 for males (Fig. 8 and 9, respectively).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn 1860, Angell\u003csup\u003e48\u003c/sup\u003e suggested that the maxilla could be expanded by opening the midpalatal suture. Halicioglu et al.\u003csup\u003e49\u003c/sup\u003e reported that orthopaedic loads generated during rapid maxillary expansion (RME) caused displacement of the bones adjacent to the maxilla, and if the structures forming the maxillary complex were unable to tolerate the force, relapse resulted through untipping of the anchorage teeth. Clinically, it has been reported that RME is indicated for patients who are still growing, and it is common that expansion treatment is unsuccessful in adults because of the fusion of the suture.\u003csup\u003e20\u003c/sup\u003e Gill et al.\u003csup\u003e50\u003c/sup\u003e specified that different treatment modalities, such as surgically assisted rapid maxillary expansion (SARME), should be used in cases in which the structure of the suture is complex, especially in adults. However, circumaxillary sutures are also important because of their resistance to expansion forces. The criterion for choosing conventional RME or SARME is usually chronological age. However, there is no SARME age consensus indicated in the literature.\u003csup\u003e20\u003c/sup\u003e Studies\u003csup\u003e10\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e19\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e51\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e52\u003c/sup\u003e on the morphology and maturation stage of the midpalatal suture and the time at which fusion occurs have revealed that the time and progress of fusion are quite varied. Melsen\u003csup\u003e9\u003c/sup\u003e reported that the morphology of the midpalatal suture changed at every stage of development and that it progressed with age, along with improvements in skeletal maturation and as the density of bone around the suture increased.\u003csup\u003e53\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eSeveral clinical investigations have reported that midpalatal suture fusion occurs via the use of occlusal radiographs\u003csup\u003e15\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e16\u003c/sup\u003e and tomography. \u003csup\u003e1\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e17-28\u003c/sup\u003e However, occlusal radiographs are not reliable for analysing the morphology of the midpalatal suture because the midpalatal area is overlain by the vomer and the structures of the external nose, so there may be incorrect radiological interpretation.\u003csup\u003e10\u003c/sup\u003e CT and CBCT are alternative methods that can provide three-dimensional and high-resolution images of craniofacial structures.\u003csup\u003e31\u003c/sup\u003e Korbmacher et al.\u003csup\u003e4\u003c/sup\u003e evaluated the maturation of the midpalatal suture of human specimens using micro-CT. However, the clinical evaluation of the midpalatal suture using micro-CT is not practical.\u003csup\u003e54\u003c/sup\u003e Franchi et al.\u003csup\u003e18\u003c/sup\u003e performed measurements of the maxillary bone around the midpalatal suture in Hounsfield units using a low dose of CT after RME expansion and at the end of the retention period. The density of the maxillary bone and the midpalatal suture was measured in Hounsfield units by Acar\u003csup\u003e32\u003c/sup\u003e on CT images taken before and after RME, and the correlation between the amount of dental and skeletal expansion and the measurement of bone density was evaluated. Using CBCT, a scan is completed by a single rotation in a short period of time, the image artifact is reduced by rapid scanning, and reconstruction without magnification is carried out.\u003csup\u003e30\u003c/sup\u003e At the same time, it is possible to visualise the midpalatal suture in vivo by avoiding the superposition of anatomical structures.\u003csup\u003e55\u003c/sup\u003e However, qualitative and quantitative analysis of the midpalatal suture can be facilitated using CBCT.\u003csup\u003e25\u003c/sup\u003e In the present study, we aimed to evaluate the relationship between chronological age and the maturation stage of the midpalatal suture and to obtain parameters that may provide concrete data about the stages of maturation.\u003c/p\u003e\n\u003cp\u003eCBCT images of 515 patients (315 females, 200 males) aged 6 to 26 years were used for our investigation (Table 4). The average age of the participants in the sample group was 16 \u0026plusmn; 3.6 years (female subjects 16.1 \u0026plusmn; 3.6 years and male subjects 15.7 \u0026plusmn; 3.7 years). The recommended age for expansion using SARME compared to RME varies\u003csup\u003e56-58\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e and Alpern and Yurosko\u003csup\u003e59\u003c/sup\u003e suggested that SARME should be considered for males over the age of 25 and females over the age of 20. It was therefore decided that the age of the individuals should not be older than 26 years in the present study. In addition, there are ethical concerns regarding the exposure of subjects to unnecessary radiation.\u003csup\u003e23\u003c/sup\u003e According to the power analysis, the number of patients required should be as high as possible, so the present study included the greatest number of patients in the literature thus far\u003csup\u003e1\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e19\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e22-28\u003c/sup\u003e (Table 2).\u003c/p\u003e\n\u003cp\u003eCBCT images were first examined using conventional methods to evaluate the maturation of the midpalatal suture.\u003csup\u003e1\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e22\u003c/sup\u003e However, conventional methods have limitations related to the likelihood that structures will look different depending on the position of the slice, which may cause misinterpretation.\u003csup\u003e23\u003c/sup\u003e Midpalatal suture maturation was first evaluated based on a five-stage classification system described by Angelieri et al.\u003csup\u003e1\u003c/sup\u003e and subsequently applied in many studies\u003csup\u003e19\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e20\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e22-28\u003c/sup\u003e that evaluated the maturation of the midpalatal suture (Table 1). Haghanifar et al.\u003csup\u003e26\u003c/sup\u003e modified this classification and, in addition to the described steps, noted that the anterior segment of the suture (in front of the nasopalatine foramen) was similar to stage C and that the posterior segment resembled stage D. This new form arose between stages C and D and is called stage CD. Stage D occurs after stage C. In stage CD, Haghanifar et al.\u003csup\u003e26\u003c/sup\u003e mentioned is accepted as stage D as defined by Angelieri et al.\u003csup\u003e1\u003c/sup\u003e in the present study because of fusion in the palatinal bone.\u003c/p\u003e\n\u003cp\u003eIn the second phase, fractal analysis, which allows objective clinical assessment of midpalatal suture maturation, was performed. A narrow box from the rear of the incisive canal to the posterior nasal spine was created as the ROI, following the recommendation of Kwak et al.\u003csup\u003e22\u003c/sup\u003e However, the radiopaque region that could affect FD calculation was excluded as much as possible. Previous studies\u003csup\u003e60\u003c/sup\u003e have indicated that the FD value is not affected by irradiation parameters, the angle of X-ray projection or the selection of the ROI. ROI selection was performed again on a random group of 100 CBCT images, and fractal analysis was conducted to evaluate intraexaminer reliability between the measurements. The Cronbach\u0026rsquo;s alpha coefficient was 0.690, and there was good intraexaminer reliability between the first and second measurements. In the present study, the box-counting method was also applied to investigate the midpalatal suture using fractal analysis, and processing of the ROIs was generally based on the method developed by White and Rudolph\u003csup\u003e47\u003c/sup\u003e. However, Geraets and van der Stelt\u003csup\u003e61\u003c/sup\u003e reported that FD may be different according to the method used in their study on bone disease, which was assessed using fractal analysis. In addition, it was also stated that errors related to the selection of ROIs and the methods used to process the images may affect the FD value.\u003csup\u003e22\u003c/sup\u003e Kwak et al.\u003csup\u003e22\u003c/sup\u003e reported that a more stable method of calculating FD for clinical use should be established and that the accuracy of the method may be improved by minimising the number of required calculation steps.\u003c/p\u003e\n\u003cp\u003eAccording to previous studies\u003csup\u003e1\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e19\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e23\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e26-28\u003c/sup\u003e of the midpalatal suture, there is a difference between the periods in which the maturational stages are observed in females and males. In the present study, there was no fusion of the midpalatal suture in individuals younger than 11 years, except for a boy aged 10 years, and these results are similar to those of Angelieri et al.\u003csup\u003e1\u003c/sup\u003e who assessed a Brazilian population. Stage D was observed in females aged 17.6 \u0026plusmn; 3.9 years and males aged 16.6 \u0026plusmn; 3.6 years. The female and male subjects had stage D disease at least 11.2 and 12.1 years, respectively. Stage E was observed in females aged 16.2 \u0026plusmn; 3.4 years and males aged 18.7 \u0026plusmn; 4.06 years. The female and male subjects had stage E disease at least 11.3 and 10.1 years, respectively. According to the results of Melsen\u0026apos;s research,\u003csup\u003e9\u003c/sup\u003e transverse growth of the midpalatal suture continues in females aged 16 years and in males aged 18, and the findings of the present study support these findings. Previous studies\u003csup\u003e1\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e9\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e19\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e23\u003c/sup\u003e have indicated that the time at which the midpalatal suture is fused is inconsistent. It has been reported \u003csup\u003e26\u003c/sup\u003e in an Iranian population that stage D, when fusion starts, was observed only in individuals over 40 years of age, and stage E was mostly observed in individuals over 50 years of age. In a study\u003csup\u003e27\u003c/sup\u003e involving adult Brazilian individuals, stages D and E were observed in subjects with a mean age of 32.3 \u0026plusmn; 14.2 years and 38.7 \u0026plusmn; 15.4 years, respectively. Tonello et al.\u003csup\u003e28\u003c/sup\u003e reported that stage D was more common in 14- and 15-year-olds, and stage E was more prevalent in those aged 14 and 15 years, except for a girl aged 12 years. Although there was a statistically significant relationship between chronologic age and maturation of the midpalatal suture in the present study, there was no correlation according to \u003cstrong\u003eS\u003c/strong\u003epearman\u0026rsquo;s correlation coefficient (Table 10). It is therefore considered that chronological age is not reliable for determining the maturation of the midpalatal suture. Korbmacher et al.\u003csup\u003e4\u003c/sup\u003e reported that the level of interdigitation and the time of midpalatal fusion were independent of chronological age. However, Haghanifar et al.\u003csup\u003e26\u003c/sup\u003e reported that there was a strong correlation between age group and midpalatal suture maturation stage, that the level of ossification increased with age, and that the duration and extent of ossification and morphology varied widely among the different age groups. However, Haghanifar et al.\u003csup\u003e26\u003c/sup\u003e reported that chronologic age was not a reliable factor for determining the maturation stage of the midpalatal suture and that maturation should be determined using CBCT in all patients. Angelieri et al.\u003csup\u003e19\u003c/sup\u003e reported that chronological age may be a viable alternative for predicting some midpalatal suture stages (particularly the early stages), and Gr\u0026uuml;nheid et al.\u003csup\u003e25\u003c/sup\u003e reported that chronologic age cannot be considered a useful parameter for predicting the maturation of the suture. When chronological age and sex were compared, the only statistically significant difference was found between female and male individuals at stage E. In contrast to the present findings, Nguyen et al.\u003csup\u003e17\u003c/sup\u003e and Haghanifar et al.\u003csup\u003e26\u003c/sup\u003e reported that there was no relationship between the fusion of the midpalatal suture and sex. This contradiction is likely the result of differences in sample size, study population, and methods used for assessing the midpalatal suture.\u003c/p\u003e\n\u003cp\u003eAccording to the results of the present study, the relationship between FD and the maturation stage of the midpalatal suture was found to be statistically significant for females and males, and there was a weak positive statistically significant correlation between midpalatal suture maturation and FD in male and female subjects (Table 9 and Fig. 7). These results contradict the results of Kwak et al.\u003csup\u003e22\u003c/sup\u003e reported that there was a strong negative correlation between FD and maturation stage. These contrary results are due to differences in the methods used during the processing of the ROIs. In the present study, the analysis was performed by the box counting method based on the system developed by White and Rudolph\u003csup\u003e47\u003c/sup\u003e, and the \u0026quot;\u003cem\u003eInvert\u003c/em\u003e\u0026quot; option was used, which varies from the methods of Kwak et al.\u003csup\u003e22\u003c/sup\u003e and Arsan\u003csup\u003e46\u003c/sup\u003e, in which the main lines of the trabecular bone were revealed. A histological study by Melsen,\u003csup\u003e9\u003c/sup\u003e in which suture morphology was evaluated, reported that at birth, the suture was broad and slightly sinuous, after which it later developed into a typical squamous suture, of which the palatine part covered the maxillary part. During puberty, the course of the suture was again slightly sinuous. Angelieri et al.\u003csup\u003e1\u003c/sup\u003e classified the maturation stages of the midpalatal suture compared to the histological morphology of the suture\u003csup\u003e2\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e8\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e9\u003c/sup\u003e and defined the suture as almost a straight high-density osseous line that appears as a scalloped irregular shape. If the FD is high, then the architecture of the bone is more complex and dense;\u003csup\u003e41\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e42\u003c/sup\u003e consequently, it is expected that FD and the maturation stage are positively correlated because the structure becomes more complex as maturation progresses. Fractal analysis was found to be a statistically significant method for predicting dichotomous maturation stage results for females and males in this study. The optimal FD cut-off value was 0.942 for females (Fig. 8) and 0.948 for males (Fig. 9). Kwak et al.\u003csup\u003e22\u003c/sup\u003e reported that the optimal FD cut-off value was 1.0235 for all subjects in a study that included 131 patients. Kang et al.\u003csup\u003e24\u003c/sup\u003e ROC curves were used to determine the cut-off values for the identification of pubertal growth spurts in females and males, and the optimal FD cut-off values for the midpalatal suture during pubertal growth were 0.9484 in males and 1.1205 in females. There appears to be a narrow range between 0.925 and 1.004 in the distribution of FD of the midpalatal suture in the present study. Therefore, investigations involving a greater sample size and age range may provide more reliable data.\u003c/p\u003e\n\u003cp\u003eIt should be noted that Isfeld et al.\u003csup\u003e54\u003c/sup\u003e assessed the different methods that were used to evaluate midpalatal maturation and stated that all new methods needed to be validated by histological references and further indicated that the use of multiple diagnostic criteria is extremely important for clinicians to accurately determine the maturation stage of the midpalatal suture. However, the use of CBCT and the choice of scanning protocol rely on good practice related to the image quality needed for the diagnostic task and the level of radiation exposure to the patient.\u003csup\u003e62\u003c/sup\u003e It is considered that it would have been more accurate to perform CBCT only on the maxilla; however, CBCT could not be performed due to the retrospective nature of the present study.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe results of the present study support the concept that using CBCT images to determine the morphology and degree of ossification of the midpalatal suture is valid. Although there was no significant correlation between midpalatal suture maturation and chronologic age, concrete findings were obtained that could assist clinicians in diagnosis and treatment planning when reaching the age and optimum FD cut-off value associated with suture fusion.\u003c/p\u003e \u003cp\u003eExamination of midpalatal suture maturation with different parameters providing quantitative data such as measuring the density ratio of the suture using CBCT images and evaluating the relationships associated with skeletal maturation indices, which are frequently used for orthodontic diagnosis and treatment planning, can provide more beneficial results.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMicro Computerised Tomography Micro CT\u003c/p\u003e\n\u003cp\u003eCone-beam computed tomography CBCT\u003c/p\u003e\n\u003cp\u003eComputed tomography CT\u003c/p\u003e\n\u003cp\u003eFractal dimension FD\u003c/p\u003e\n\u003cp\u003eRegion of interest ROI\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCBCT images at the archive of the Department of Oral and Maxillofacial Radiology, Abant Izzet Baysal University, Faculty of Dentistry, were used in this retrospective study. Since informed consent forms (permitting the use of data for academic research) were routinely obtained from all patients before taking CBCT scans by the Department of Oral and Maxillofacial Radiology, Abant Izzet Baysal University, there was no need to contact patients again or request consent forms. The Institutional Clinical Research and Ethics Committee of Abant Izzet Baysal University approved the study protocol (reference number: 20116/85 and the date: 24.11.2016).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData-availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable reques.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared that this study has received no financial support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConcept \u0026ndash; G.K., K.H.; Design \u0026ndash; G.K., K.H., S.H.; Data Collection and/or Processing \u0026ndash; G.K., S.H.; Analysis and/or Interpretation \u0026ndash; G.K., S.H.; Literature Review G.K., K.H., S.H.; Writing \u0026ndash; G.K., S.H; Critical Review \u0026ndash; G.K., K.H., S.H.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAngelieri F, Cevidanes LH, Franchi L, Goncalves JR, Benavides E, McNamara JA. 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The relationship of rapid maxillary expansion to surgery with special reference to midpalatal synostosis. Br J Oral Surg 1981;19:180-96.\u003c/li\u003e\n\u003cli\u003eAlpern MC, Yurosko JJ. Rapid palatal expansion in adults: with and without surgery. Angle Orthod 1987;57:245-63.\u003c/li\u003e\n\u003cli\u003eShrout MK, Potter NJ, Mailhot JM, Hildebolt CF. Morphologic operations used to distinguish between two patient populations differing in periodontal health. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 1998;85:334-8.\u003c/li\u003e\n\u003cli\u003eGeraets WG, Van der Stelt PF. Fractal properties of bone. Dentomaxillofac Radiol 2000;29:144-53.\u003c/li\u003e\n\u003cli\u003eAl-Okshi A, Lindh C, Sale H, Gunnarson M, Rohlin M. Effective dose of cone beam CT (CBCT) of the facial skeleton: a systematic review. Br J Radiol 2015; 88:20140658\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. The distribution of maturation stages and chronologic age of the studies related to the midpalatal suture. (n: Number of patients, sd: Standard deviation, Min: Minimum, Max: Maximum)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.458333333333334%\" style=\"width: 9.4052%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" style=\"width: 7.798%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.166666666666668%\" colspan=\"5\" style=\"width: 25.4775%;\"\u003e\n \u003cp\u003eAngelieri et al.\u003csup\u003e1\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.875%\" colspan=\"5\" style=\"width: 33.0374%;\"\u003e\n \u003cp\u003eAngelieri et al.\u003csup\u003e19\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.956521739130435%\" style=\"width: 9.4052%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.695652173913043%\" style=\"width: 7.798%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaturation Stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" style=\"width: 6.6075%;\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.695652173913043%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.695652173913043%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\" style=\"width: 8.6314%;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.695652173913043%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\" style=\"width: 3.3335%;\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.50537634408602%\" colspan=\"2\" style=\"width: 17.2032%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\" style=\"width: 6.6075%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" style=\"width: 8.6314%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" style=\"width: 3.3335%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.956521739130435%\" style=\"width: 9.4052%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.695652173913043%\" style=\"width: 7.798%;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" style=\"width: 6.6075%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.695652173913043%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.695652173913043%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\" style=\"width: 8.6314%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.695652173913043%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\" style=\"width: 3.3335%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.956521739130435%\" rowspan=\"3\" style=\"width: 9.4052%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronologic age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.695652173913043%\" style=\"width: 7.798%;\"\u003e\n \u003cp\u003eMean \u0026plusmn;sd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" style=\"width: 6.6075%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.695652173913043%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e6.2 \u0026plusmn; 0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.695652173913043%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e9 \u0026plusmn; 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\" style=\"width: 8.6314%;\"\u003e\n \u003cp\u003e14.8 \u0026plusmn; 9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.695652173913043%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e21.7 \u0026plusmn; 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\" style=\"width: 3.3335%;\"\u003e\n \u003cp\u003e20.1 \u0026plusmn; 11.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.876543209876543%\" style=\"width: 7.798%;\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\" style=\"width: 6.6075%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.876543209876543%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.876543209876543%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 8.6314%;\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.876543209876543%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.345679012345679%\" style=\"width: 3.3335%;\"\u003e\n \u003cp\u003e12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.876543209876543%\" style=\"width: 7.798%;\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\" style=\"width: 6.6075%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.876543209876543%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.876543209876543%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 8.6314%;\"\u003e\n \u003cp\u003e58.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.876543209876543%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e47.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.345679012345679%\" style=\"width: 3.3335%;\"\u003e\n \u003cp\u003e55.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.50537634408602%\" colspan=\"2\" style=\"width: 17.2032%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\" style=\"width: 6.6075%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" style=\"width: 8.6314%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" style=\"width: 3.3335%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.956521739130435%\" style=\"width: 9.4052%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.695652173913043%\" style=\"width: 7.798%;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" style=\"width: 6.6075%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.695652173913043%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.695652173913043%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\" style=\"width: 8.6314%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.695652173913043%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\" style=\"width: 3.3335%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.956521739130435%\" rowspan=\"3\" style=\"width: 9.4052%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronologic age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.695652173913043%\" style=\"width: 7.798%;\"\u003e\n \u003cp\u003eMean \u0026plusmn;sd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" style=\"width: 6.6075%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.695652173913043%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e6.5 \u0026plusmn; 1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.695652173913043%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e11.6 \u0026plusmn; 1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\" style=\"width: 8.6314%;\"\u003e\n \u003cp\u003e14.4 \u0026plusmn; 3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.695652173913043%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e24.9 \u0026plusmn; 9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\" style=\"width: 3.3335%;\"\u003e\n \u003cp\u003e32.7 \u0026plusmn; 11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.876543209876543%\" style=\"width: 7.798%;\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\" style=\"width: 6.6075%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.876543209876543%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.876543209876543%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 8.6314%;\"\u003e\n \u003cp\u003e10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.876543209876543%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.345679012345679%\" style=\"width: 3.3335%;\"\u003e\n \u003cp\u003e18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.876543209876543%\" style=\"width: 7.798%;\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\" style=\"width: 4.7026%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\" style=\"width: 6.6075%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.876543209876543%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.876543209876543%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 8.6314%;\"\u003e\n \u003cp\u003e26.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.876543209876543%\" style=\"width: 7.7385%;\"\u003e\n \u003cp\u003e37.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.345679012345679%\" style=\"width: 3.3335%;\"\u003e\n \u003cp\u003e44.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. The distribution of maturation stages and chronologic age of the studies related to the midpalatal suture. (n: Number of patients, sd: Standard deviation, Min: Minimum, Max: Maximum)\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(Continued)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" style=\"width: 13.5562%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\" style=\"width: 17.1464%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" colspan=\"5\" style=\"width: 20.2414%;\"\u003e\n \u003cp\u003eJang et al.\u003csup\u003e23\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" colspan=\"5\" style=\"width: 17.2083%;\"\u003e\n \u003cp\u003eAngelieri et al.\u003csup\u003e27\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" colspan=\"5\" style=\"width: 10.7707%;\"\u003e\n \u003cp\u003eTonello et al.\u003csup\u003e28\u003c/sup\u003e \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\" style=\"width: 13.5562%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" style=\"width: 17.1464%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaturation Stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.125%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.125%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.125%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 2.7236%;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.375%\" colspan=\"2\" style=\"width: 30.7026%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.125%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.125%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.125%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 2.7236%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\" style=\"width: 13.5562%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" style=\"width: 17.1464%;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.125%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.125%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.125%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 2.7236%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\" rowspan=\"3\" style=\"width: 13.5562%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronologic age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" style=\"width: 17.1464%;\"\u003e\n \u003cp\u003eMean \u0026plusmn;sd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.125%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.125%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.125%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 2.7236%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.25%\" style=\"width: 17.1464%;\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.75%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.75%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.75%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 2.7236%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.25%\" style=\"width: 17.1464%;\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.75%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.75%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.75%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 2.7236%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.375%\" colspan=\"2\" style=\"width: 30.7026%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.125%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.125%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.125%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 2.7236%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\" style=\"width: 13.5562%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" style=\"width: 17.1464%;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.125%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.125%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.125%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 2.7236%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\" rowspan=\"3\" style=\"width: 13.5562%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronologic age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" style=\"width: 17.1464%;\"\u003e\n \u003cp\u003eMean \u0026plusmn;sd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.125%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.125%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.125%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 2.7236%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.25%\" style=\"width: 17.1464%;\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.75%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.75%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.75%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 2.7236%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.25%\" style=\"width: 17.1464%;\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.75%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.75%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.75%\" style=\"width: 3.0331%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 4.0235%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 2.7236%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5%\" style=\"width: 1.3618%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. The studies performed about the maturation of the midpalatal suture. (n: Number of patient, sd: Standard deviation, Min: Minimum, Max: Maximum)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"988\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.088978766430738%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.448938321536906%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.96562184024267%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.695652173913043%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.087967644084934%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.71284125379171%\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronologic age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.080808080808081%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.444444444444445%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.959595959595959%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.686868686868687%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.07070707070707%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.585858585858585%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Sample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.585858585858585%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.585858585858585%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.105369807497468%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.4579533941236065%\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.977710233029382%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.713272543059777%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.180344478216819%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.673758865248227%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.268490374873354%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; sd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; sd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; sd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.105369807497468%\"\u003e\n \u003cp\u003eFranchi et al.\u003csup\u003e18\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.4579533941236065%\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.977710233029382%\"\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.713272543059777%\"\u003e\n \u003cp\u003eItalian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.180344478216819%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.673758865248227%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.268490374873354%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e11.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.105369807497468%\"\u003e\n \u003cp\u003eAcar\u003csup\u003e32\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.4579533941236065%\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.977710233029382%\"\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.713272543059777%\"\u003e\n \u003cp\u003eTurkish\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.180344478216819%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.673758865248227%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.268490374873354%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e14.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.105369807497468%\"\u003e\n \u003cp\u003eAngelieri et al.\u003csup\u003e1\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.4579533941236065%\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.977710233029382%\"\u003e\n \u003cp\u003eCBCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.713272543059777%\"\u003e\n \u003cp\u003eBrazilian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.180344478216819%\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.673758865248227%\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.268490374873354%\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e58.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.105369807497468%\"\u003e\n \u003cp\u003eAngelieri et al.\u003csup\u003e19\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.4579533941236065%\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.977710233029382%\"\u003e\n \u003cp\u003eCBCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.713272543059777%\"\u003e\n \u003cp\u003eBrazilian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.180344478216819%\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.673758865248227%\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.268490374873354%\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e14.8 \u0026plusmn; 9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e58.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e55.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e44.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.105369807497468%\"\u003e\n \u003cp\u003eKwak et al.\u003csup\u003e22\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.4579533941236065%\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.977710233029382%\"\u003e\n \u003cp\u003eCBCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.713272543059777%\"\u003e\n \u003cp\u003eKorean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.180344478216819%\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.673758865248227%\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.268490374873354%\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e24.1 \u0026plusmn; 5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e53.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e25.2 \u0026plusmn; 5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e23.1 \u0026plusmn; 5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.105369807497468%\"\u003e\n \u003cp\u003eJang et al.\u003csup\u003e23\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.4579533941236065%\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.977710233029382%\"\u003e\n \u003cp\u003eCBCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.713272543059777%\"\u003e\n \u003cp\u003eKorean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.180344478216819%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.673758865248227%\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.268490374873354%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e12.03 \u0026plusmn; 3.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e13.56 \u0026plusmn; 3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e14.3 \u0026plusmn; 3.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.105369807497468%\"\u003e\n \u003cp\u003eKang et al.\u003csup\u003e24\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.4579533941236065%\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.977710233029382%\"\u003e\n \u003cp\u003eCBCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.713272543059777%\"\u003e\n \u003cp\u003eKorean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.180344478216819%\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.673758865248227%\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.268490374873354%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e15.5 \u0026plusmn; 7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.105369807497468%\"\u003e\n \u003cp\u003eGr\u0026uuml;nheid et al.\u003csup\u003e25\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.4579533941236065%\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.977710233029382%\"\u003e\n \u003cp\u003eCBCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.713272543059777%\"\u003e\n \u003cp\u003eAmerican\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.180344478216819%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.673758865248227%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.268490374873354%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e12.9 \u0026plusmn; 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.105369807497468%\"\u003e\n \u003cp\u003eHaghanifar et al.\u003csup\u003e26\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.4579533941236065%\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.977710233029382%\"\u003e\n \u003cp\u003eCBCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.713272543059777%\"\u003e\n \u003cp\u003eIran\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.180344478216819%\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.673758865248227%\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.268490374873354%\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e39.62 \u0026plusmn;1 7.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e39.83 \u0026plusmn; 17.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e39.42 \u0026plusmn; 17.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.105369807497468%\"\u003e\n \u003cp\u003eAngelieri ve ark.\u003csup\u003e27\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.4579533941236065%\"\u003e\n \u003cp\u003e20167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.977710233029382%\"\u003e\n \u003cp\u003eCBCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.713272543059777%\"\u003e\n \u003cp\u003eBrazilian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.180344478216819%\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.673758865248227%\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.268490374873354%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e36.4 \u0026plusmn; 15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.105369807497468%\"\u003e\n \u003cp\u003eTonello ve ark.\u003csup\u003e28\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.4579533941236065%\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.977710233029382%\"\u003e\n \u003cp\u003eCBCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.713272543059777%\"\u003e\n \u003cp\u003eBrazilian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.180344478216819%\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.673758865248227%\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.268490374873354%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.625126646403242%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.052684903748734%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.86322188449848%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eThe criterias of the radioragraphs that were not included for the study\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"693\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.78354978354978%\"\u003e\n \u003cp\u003ea history of previous orthodontic treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50.21645021645022%\"\u003e\n \u003cp\u003econgenital bone defects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.78354978354978%\"\u003e\n \u003cp\u003eLesions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50.21645021645022%\"\u003e\n \u003cp\u003ecleft palate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.78354978354978%\"\u003e\n \u003cp\u003eincisive canal cyst\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50.21645021645022%\"\u003e\n \u003cp\u003ethe field of view not included the midpalatal suture\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.78354978354978%\"\u003e\n \u003cp\u003eimpacted teeth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50.21645021645022%\"\u003e\n \u003cp\u003ethe poor-quality images\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.78354978354978%\"\u003e\n \u003cp\u003esinus pneumatization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50.21645021645022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Gender and demographics of the sample. (n: Number of patients, sd: Standard deviation,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMin: Minimum, Max: Maximum\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"613\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.146579804560261%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.146579804560261%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;sd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.54071661237785%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"top\"\u003e\n \u003cp\u003e315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.146579804560261%\" valign=\"top\"\u003e\n \u003cp\u003e61.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.146579804560261%\" valign=\"top\"\u003e\n \u003cp\u003e16.1\u0026nbsp;\u0026plusmn;\u0026nbsp;3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.54071661237785%\" valign=\"top\"\u003e\n \u003cp\u003e15.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"top\"\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.146579804560261%\" valign=\"top\"\u003e\n \u003cp\u003e38.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.146579804560261%\" valign=\"top\"\u003e\n \u003cp\u003e15.7\u0026nbsp;\u0026plusmn;\u0026nbsp;3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.54071661237785%\" valign=\"top\"\u003e\n \u003cp\u003e15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"top\"\u003e\n \u003cp\u003eTotal Sample\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"top\"\u003e\n \u003cp\u003e515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.146579804560261%\" valign=\"top\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.146579804560261%\" valign=\"top\"\u003e\n \u003cp\u003e16.0\u0026nbsp;\u0026plusmn;\u0026nbsp;3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.54071661237785%\" valign=\"top\"\u003e\n \u003cp\u003e15.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"top\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.37785016286645%\" valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Distribution of the maturation stages and mean FD. (n: Number of patients,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003esd: Standard deviation,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMin: Minimum, Max: Maximum)\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.785123966942149%\" style=\"width: 4.9832%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.17355371900826%\" colspan=\"3\" style=\"width: 33.775%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.17355371900826%\" colspan=\"3\" style=\"width: 33.775%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.421487603305785%\" style=\"width: 8.1669%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.785123966942149%\" style=\"width: 4.9832%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.107438016528926%\" style=\"width: 6.0906%;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.867768595041323%\" style=\"width: 13.7038%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; sd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\" style=\"width: 13.9806%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Min-Max)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.429752066115702%\" style=\"width: 7.1979%;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.545454545454545%\" style=\"width: 12.5964%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; sd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\" style=\"width: 13.9806%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Min-Max)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.421487603305785%\" style=\"width: 8.1669%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.785123966942149%\" style=\"width: 4.9832%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.107438016528926%\" style=\"width: 6.0906%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.867768595041323%\" style=\"width: 13.7038%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\" style=\"width: 13.9806%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.429752066115702%\" style=\"width: 7.1979%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.545454545454545%\" style=\"width: 12.5964%;\"\u003e\n \u003cp\u003e0.92\u0026nbsp;\u0026plusmn;\u0026nbsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\" style=\"width: 13.9806%;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003cp\u003e(0.89-0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.421487603305785%\" style=\"width: 8.1669%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.785123966942149%\" style=\"width: 4.9832%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.107438016528926%\" style=\"width: 6.0906%;\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.867768595041323%\" style=\"width: 13.7038%;\"\u003e\n \u003cp\u003e0.93\u0026nbsp;\u0026plusmn;\u0026nbsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\" style=\"width: 13.9806%;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003cp\u003e(0.82-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.429752066115702%\" style=\"width: 7.1979%;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.545454545454545%\" style=\"width: 12.5964%;\"\u003e\n \u003cp\u003e0.94 \u0026plusmn; 0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\" style=\"width: 13.9806%;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003cp\u003e(0.85-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.421487603305785%\" style=\"width: 8.1669%;\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.785123966942149%\" style=\"width: 4.9832%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.107438016528926%\" style=\"width: 6.0906%;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.867768595041323%\" style=\"width: 13.7038%;\"\u003e\n \u003cp\u003e0.94\u0026nbsp;\u0026plusmn;\u0026nbsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\" style=\"width: 13.9806%;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003cp\u003e(0.83-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.429752066115702%\" style=\"width: 7.1979%;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.545454545454545%\" style=\"width: 12.5964%;\"\u003e\n \u003cp\u003e0.94 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\" style=\"width: 13.9806%;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003cp\u003e(0.85-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.421487603305785%\" style=\"width: 8.1669%;\"\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.785123966942149%\" style=\"width: 4.9832%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.107438016528926%\" style=\"width: 6.0906%;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.867768595041323%\" style=\"width: 13.7038%;\"\u003e\n \u003cp\u003e0.95\u0026nbsp;\u0026plusmn;\u0026nbsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\" style=\"width: 13.9806%;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003cp\u003e(0.89-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.429752066115702%\" style=\"width: 7.1979%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.545454545454545%\" style=\"width: 12.5964%;\"\u003e\n \u003cp\u003e0.94 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\" style=\"width: 13.9806%;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003cp\u003e(0.92-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.421487603305785%\" style=\"width: 8.1669%;\"\u003e\n \u003cp\u003e0.848\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.785123966942149%\" style=\"width: 4.9832%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.107438016528926%\" style=\"width: 6.0906%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.867768595041323%\" style=\"width: 13.7038%;\"\u003e\n \u003cp\u003e0.95\u0026nbsp;\u0026plusmn;\u0026nbsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\" style=\"width: 13.9806%;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003cp\u003e(0.93-0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.429752066115702%\" style=\"width: 7.1979%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.545454545454545%\" style=\"width: 12.5964%;\"\u003e\n \u003cp\u003e0.95 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.198347107438018%\" style=\"width: 13.9806%;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003cp\u003e(0.89-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.421487603305785%\" style=\"width: 8.1669%;\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. Distribution of the maturation stages and mean chronologic age. (n: Number of patients,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003esd: Standard deviation,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMin: Minimum, Max: Maximum\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.785123966942149%\" valign=\"top\" style=\"width: 4.9861%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.51239669421488%\" colspan=\"3\" style=\"width: 33.1025%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.33057851239669%\" colspan=\"3\" style=\"width: 34.626%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.421487603305785%\" style=\"width: 8.1717%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.766062602965404%\" valign=\"top\" style=\"width: 4.9861%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0840197693574956%\" style=\"width: 6.0942%;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321252059308073%\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; sd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.14497528830313%\" style=\"width: 13.7119%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedyan\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Min-Max)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.060955518945635%\" style=\"width: 7.7562%;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.156507413509061%\" style=\"width: 13.0194%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; sd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.14497528830313%\" style=\"width: 13.7119%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedyan\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Min-Max)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.390444810543658%\" style=\"width: 8.1717%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.766062602965404%\" style=\"width: 4.9861%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0840197693574956%\" style=\"width: 6.0942%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321252059308073%\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.14497528830313%\" style=\"width: 13.7119%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.060955518945635%\" style=\"width: 7.7562%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.156507413509061%\" style=\"width: 13.0194%;\"\u003e\n \u003cp\u003e12.9 \u0026plusmn; \u0026nbsp;1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.14497528830313%\" style=\"width: 13.7119%;\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003cp\u003e(11.5-13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.390444810543658%\" style=\"width: 8.1717%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.766062602965404%\" style=\"width: 4.9861%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0840197693574956%\" style=\"width: 6.0942%;\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321252059308073%\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003e16.2 \u0026plusmn; 3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.14497528830313%\" style=\"width: 13.7119%;\"\u003e\n \u003cp\u003e15.7\u003c/p\u003e\n \u003cp\u003e(8.8-24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.060955518945635%\" style=\"width: 7.7562%;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.156507413509061%\" style=\"width: 13.0194%;\"\u003e\n \u003cp\u003e15.9\u0026nbsp;\u0026plusmn;\u0026nbsp;3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.14497528830313%\" style=\"width: 13.7119%;\"\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003cp\u003e(6.5-25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.390444810543658%\" style=\"width: 8.1717%;\"\u003e\n \u003cp\u003e0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.766062602965404%\" style=\"width: 4.9861%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0840197693574956%\" style=\"width: 6.0942%;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321252059308073%\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003e15.4 \u0026plusmn; \u0026nbsp;3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.14497528830313%\" style=\"width: 13.7119%;\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003cp\u003e(9-26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.060955518945635%\" style=\"width: 7.7562%;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.156507413509061%\" style=\"width: 13.0194%;\"\u003e\n \u003cp\u003e14.9\u0026nbsp;\u0026plusmn;\u0026nbsp;3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.14497528830313%\" style=\"width: 13.7119%;\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003cp\u003e(6-26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.390444810543658%\" style=\"width: 8.1717%;\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.766062602965404%\" style=\"width: 4.9861%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0840197693574956%\" style=\"width: 6.0942%;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321252059308073%\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003e17.6 \u0026plusmn; \u0026nbsp;3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.14497528830313%\" style=\"width: 13.7119%;\"\u003e\n \u003cp\u003e17.6\u003c/p\u003e\n \u003cp\u003e(11.2-25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.060955518945635%\" style=\"width: 7.7562%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.156507413509061%\" style=\"width: 13.0194%;\"\u003e\n \u003cp\u003e16.6\u0026nbsp;\u0026plusmn;\u0026nbsp;3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.14497528830313%\" style=\"width: 13.7119%;\"\u003e\n \u003cp\u003e16.2\u003c/p\u003e\n \u003cp\u003e(12.1-23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.390444810543658%\" style=\"width: 8.1717%;\"\u003e\n \u003cp\u003e0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.766062602965404%\" style=\"width: 4.9861%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0840197693574956%\" style=\"width: 6.0942%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321252059308073%\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003e16.2 \u0026plusmn; 3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.14497528830313%\" style=\"width: 13.7119%;\"\u003e\n \u003cp\u003e15.8\u003c/p\u003e\n \u003cp\u003e(11.3-23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.060955518945635%\" style=\"width: 7.7562%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.156507413509061%\" style=\"width: 13.0194%;\"\u003e\n \u003cp\u003e18.7\u0026nbsp;\u0026plusmn;\u0026nbsp;4.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.14497528830313%\" style=\"width: 13.7119%;\"\u003e\n \u003cp\u003e18.5\u003c/p\u003e\n \u003cp\u003e(10.1-26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.390444810543658%\" style=\"width: 8.1717%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.049\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7. Binary comparison of the statistically significant relationship between FD and the maturarion stages of the midpalatal suture.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.266998341625207%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.633499170812604%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.764510779436153%\"\u003e\n \u003cp\u003e\u003cstrong\u003eB-C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.930348258706468%\"\u003e\n \u003cp\u003e\u003cstrong\u003eB-D\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.764510779436153%\"\u003e\n \u003cp\u003e\u003cstrong\u003eB-E\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.764510779436153%\"\u003e\n \u003cp\u003e\u003cstrong\u003eC-D\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.096185737976782%\"\u003e\n \u003cp\u003e\u003cstrong\u003eC-E\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.779436152570481%\"\u003e\n \u003cp\u003e\u003cstrong\u003eD-E\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.266998341625207%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.633499170812604%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.764510779436153%\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.930348258706468%\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.764510779436153%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.764510779436153%\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.096185737976782%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.779436152570481%\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.266998341625207%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.633499170812604%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.764510779436153%\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.930348258706468%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.764510779436153%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.764510779436153%\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.096185737976782%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.779436152570481%\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8. Binary comparison of the statistically significant relationship between chronologic age and the maturarion stages of the midpalatal suture.\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"589\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.62862010221465%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.814310051107325%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\"\u003e\n \u003cp\u003e\u003cstrong\u003eB-C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.310051107325384%\"\u003e\n \u003cp\u003e\u003cstrong\u003eB-D\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\"\u003e\n \u003cp\u003e\u003cstrong\u003eB-E\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\"\u003e\n \u003cp\u003e\u003cstrong\u003eC-D\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.480408858603067%\"\u003e\n \u003cp\u003e\u003cstrong\u003eC-E\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\"\u003e\n \u003cp\u003e\u003cstrong\u003eD-E\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.62862010221465%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.814310051107325%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.310051107325384%\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.480408858603067%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.62862010221465%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.814310051107325%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.310051107325384%\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.480408858603067%\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 9. Spearman\u0026rsquo;s correlation coeffcients for maturation stage and FD.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"603\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.26622296173045%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.97171381031614%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.97171381031614%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.79034941763727%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.26622296173045%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation cofficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.97171381031614%\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.97171381031614%\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.79034941763727%\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.26622296173045%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.97171381031614%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.97171381031614%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.79034941763727%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 10.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSpearman\u0026rsquo;s correlation coeffcients for the maturation stage and chronologic age.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.43261231281198%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.9567387687188%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.43261231281198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation cofficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.9567387687188%\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.43261231281198%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\"\u003e\n \u003cp\u003e0.873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.9567387687188%\"\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Midpalatal suture, CBCT, Fractal analysis","lastPublishedDoi":"10.21203/rs.3.rs-4184630/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4184630/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe relationship between midpalatal suture maturation and chronological age was evaluated via cone-beam computed tomography (CBCT) via fractal analysis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eCone-beam computed tomography images of 515 subjects with a mean age of 16\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6 years were included in the study. Midpalatal suture maturation was evaluated based on the classification described by Angelieri et al. In the second stage, the evaluation was conducted through quantitative data obtained by fractal analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThere was a statistically significant difference between the fractal dimension and chronologic age related to the maturation of the midpalatal suture. There was a weak positive statistically significant correlation between the maturation of the midpalatal suture and the fractal dimension, but there was no statistically significant correlation between the maturation stage and the chronological age of the subjects.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eFractal analysis can be used to determine the maturation stages of the midpalatal suture. Considering the positive correlation between the fractal dimension and maturation of the midpalatal suture, the optimal fractal dimension cut-off value can be used to assess suture fusion. Chronologic age is not a precise indicator for evaluating the maturation of the midpalatal suture, but it can offer alternative guidance regarding suture fusion.\u003c/p\u003e","manuscriptTitle":"Evaluation of the relationship between midpalatal suture maturation and chronologic age with cone-beam computerised tomography via fractal analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-08 17:44:52","doi":"10.21203/rs.3.rs-4184630/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9dbdda31-9c75-479b-8b1f-bbb6a53b354a","owner":[],"postedDate":"April 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-26T04:38:23+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-08 17:44:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4184630","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4184630","identity":"rs-4184630","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}