Precision of the Indirect Bonding Method Using CAD/CAM Technology and CBCT Image Superimposition

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The incorporation of computer-aided design/computer-aided manufacturing (CAD/CAM) approach can improve bracket positioning precision and potentially reduce treatment time. Additionally, cone-beam computed tomography (CBCT) enables three-dimensional evaluation of both crowns and roots, and the integration of digital models with CBCT data may enhance the precision of the indirect bonding process. This study aims to evaluate the precision of digital indirect bonding, with and without the superimposition of CBCT images onto digital models, as performed by orthodontists. Methods A total of 22 orthodontists participated in this study. Each orthodontist performed virtual bonding on digital models using OrthoAnalyzer™ software (3Shape A/S, Copenhagen, Denmark) software in two stages: first, visualizing only the clinical crowns, and after a minimum interval of 15 days, visualizing both the crowns and roots with the aid of CBCT images. Bracket positioning discrepancies between the two bonding sessions were measured to assess intra-operator consistency. Positional deviations were analyzed in three dimensions: vertical, horizontal, and angular. Statistical analysis was conducted at a significance level of 5%. Results Positional deviations were within clinically acceptable limits for both horizontal and vertical dimensions, with deviations measuring less than 0.5 mm. However, angular deviations exceeded the clinically acceptable threshold of 2° for certain dental elements. Conclusion The results indicate that orthodontists maintained consistent precision in the vertical and horizontal dimensions. However, the integration of CBCT data affected the precision of angular measurements. Clinical significance: These findings suggest that the incorporation of CBCT during indirect bonding may alter bracket placement in the angular dimension, which is critical for achieving optimal orthodontic outcomes. Imaging precision indirect bonding three-dimensional image computer-aided design Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Indirect bonding techniques have gained significant popularity in orthodontics due to their two-phase process, encompassing both laboratory and clinical stages [ 1 , 2 ]. Key advantages of this approach include enhanced precision in bracket placement and a reduction in chair time during clinical appointments [ 3 ]. While precision in bracket positioning is often highlighted as a primary advantage, studies comparing direct and indirect bonding methods, both in vitro and in vivo, have yielded inconsistent results [ 4 – 10 ]. The emergence of digital dentistry has introduced advancements in orthodontic diagnostics and treatment planning [ 11 ]. Computer-aided design/computer-aided manufacturing (CAD/CAM) technology enables virtual setups, virtual bracket positioning, and the fabrication of customized bonding guides using three-dimensional (3D) printers. The implementation of CAD/CAM-based indirect bonding protocols aims to minimize human error in bracket placement. Despite this, several studies have raised concerns regarding the precision of digital indirect bonding during both the planning and laboratory phases. These concerns may stem from limitations inherent to the software, the learning curve faced by practitioners, or errors during bracket transfer [ 11 , 12 ]. Conversely, research has demonstrated that CAD/CAM-based indirect bonding significantly enhances bracket positioning precision, contributing to reduced treatment times [ 13 , 14 ]. Duarte et al. [ 15 ] found that indirect bonding techniques using digital guides when designed by an experienced practitioner, can be reproducibly applied regardless of the clinician's experience. Nevertheless, no studies have investigated the reproducibility of this technique when applied by different orthodontists. Cone-beam computed tomography (CBCT) has become a valuable tool in orthodontic diagnosis and treatment planning, though it introduces greater radiation exposure and higher costs compared to conventional panoramic and cephalometric radiographs [ 16 , 17 ]. Additionally, distinct guidelines on the use of CBCT in orthodontics have been published for North America and Europe. The North American guidelines were developed by the American Academy of Oral and Maxillofacial Radiology (AAOMR) [ 18 ], while the European recommendations are part of the SEDENTEXCT project [ 19 ]. The primary difference between the North American and European recommendations can be attributed to the distinct criteria used. The North American guidelines were based on the most frequent applications of CBCT reported in the literature. In contrast, the SEDENTEXCT guidelines were strictly based on the presence of high levels of evidence regarding CBCT's efficacy [ 20 ]. Nevertheless, both sets of guidelines recommend the use of CBCT primarily for complex cases, such as impacted teeth, critical dental movements in periodontally compromised patients, and pre-surgical orthodontic assessments [ 11 ]. Moreover, in comparison to traditional two-dimensional imaging, CBCT allows for more precise evaluations of both crowns and roots. When applied to virtual bracket placement on digital models, CBCT superimposition facilitates the visualization of root inclinations, thus it may enhance the precision of appliance placement and promote improved root parallelism after treatment [ 2 ]. Despite these advantages, no studies have examined the precision of CAD/CAM-based indirect bonding in comparison to virtual bracket placement involving CBCT superimposition. This study, therefore, aims to evaluate the precision of virtual bracket placement by superimposing CBCT images onto digital models, with the bonding plans executed by various orthodontists. The null hypothesis proposes that no significant differences exist in linear or angular deviations between bonding techniques with and without CBCT integration. 2. Materials and Methods This study was initiated following approval by the Research Ethics Committee of Pedro Ernesto University Hospital, at the State University of Rio de Janeiro (UERJ) (Approval No. 4.950.248). A total of 22 orthodontists participated in the digital planning procedures for bracket positioning. Participants were recruited through the Brazilian Association of Orthodontics (ABOR) and the Orthodontic Clinic at UERJ. Inclusion criteria required participants to hold a certificate in orthodontics, maintain active registration, be currently practicing fixed orthodontic treatments, and have at least three years of experience. All participants provided written informed consent and completed a questionnaire detailing their years of clinical practice. The diagnostic records of a single patient were used for this study. These included stereolithography (STL) files representing digital dental models of the upper and lower arches and Digital Imaging and Communications in Medicine (DICOM) files containing CBCT scans (Fig. 1 ). The patient signed an informed consent form agreeing to participate in the study and to the use of their diagnostic records for research purposes. This study was conducted in accordance with the principles of the Declaration of Helsinki. The CBCT images were required for superimposition onto the digital models, and it was clinically indicated following the SEDENTEXCT guidelines for orthodontic treatment planning [ 19 ]. The selected patient was a Caucasian female with a Class II Division 1 incisor relationship, a mild Class II skeletal base, and average vertical proportions. His malocclusion was complicated by moderate crowding in the upper arch, severe crowding in the lower arch, increased overjet, and a 2mm overbite. The buccal segment relationships are presented on both sides with a class I molar relationship. The patient’s STL and DICOM files were imported into OrthoAnalyzer™ software (3Shape A/S, Copenhagen, Denmark), licensed from Compass 3D™ (Compass 3D, Belo Horizonte, Minas Gerais, Brazil), a laboratory specializing in digital orthodontic services. The software was accessed remotely throughout the study using TeamViewer™ version 15.42.7 (TeamViewer AG, Göppingen, Germany). The superimposition of CBCT images onto the digital dental models was performed using three corresponding points identified in each image. The software’s best-fit algorithm aligned these points, producing a combined model that displayed clinical crowns digitally and revealed dental roots and surrounding bony structures via tomography (Fig. 1 ). In total, 44 models were generated for the digital indirect bonding evaluation: 22 models displaying only clinical crowns and 22 models incorporating root visualizations. Moreover, Roth prescription 0.022x0.025” slot Mini Master™ brackets (American Orthodontics, Sheboygan, WI, USA) were selected within OrthoAnalyzer™ software for placement on teeth from the second premolar to the second premolar in both arches. Molars were excluded from bonding due to their limited representation in the software’s virtual library. Before the digital bracket positioning procedure, participants received comprehensive written and oral instructions on software interaction and effective command execution. The main bracket positioning instructions were as follows: (1) Adjustments were performed during remote sessions via TeamViewer™, where participants observed the OrthoAnalyzer™ interface; (2) Bracket adjustments were performed exclusively by a single experience operator (ICS) on voice commands from participants; (3) Brackets were initially positioned automatically by the software at the midpoint of the facial axis of the clinical crown on the buccal surface of each tooth, serving as the default starting point; (4) Brackets were then adjusted along three spatial planes—vertical, horizontal, and angular—using the mouse or keyboard controls on the operator's MacBook Air™ (macOS Sonoma version 14, Apple Inc., Cupertino, CA, USA). Adjustments were measured in millimeters for magnitude and degrees for angular deviations. In the first phase, participants planned bracket positioning while visualizing only the digital models of the clinical crowns (Fig. 2 A). After a minimum interval of 15 days, participants repeated the bonding process, this time incorporating CBCT superimposition for visualization of both crowns and roots (Fig. 2 B). Participants had access to CBCT data at any time during this phase. Procedures were standardized and conducted in the same location, under consistent lighting conditions, using a MacBook Air™. Following the virtual bondings, model comparisons—with and without CBCT integration—were conducted for each participant. This process, termed intra-operator superimposition (IOS), adhered to OrthoAnalyzer™ protocols. The "Model Comparison" feature was used to align dental models with and without root visualizations, including the positioned brackets, via the software's best-fit algorithm. This ensured alignment based on stable anatomical regions, eliminating the need for manual point selection (Fig. 3 A). Once aligned, the "Overlay and Display" option was activated to merge the models into a single visualization, enabling direct comparison of bracket positional changes (Fig. 3 B). This functionality provided a detailed analysis by highlighting positional discrepancies within the software interface. Measurements of positional discrepancies were conducted following superimposition to quantify differences in bracket positioning. Positional differences were analyzed by generating automated planes in OrthoAnalyzer™ software, which created sagittal, axial, and coronal sections based on the long axis and the mesial, distal, buccal, and lingual surfaces of each tooth. This methodology enabled the evaluation of bracket positioning across three spatial planes: vertical (occluso-gingival), horizontal (mesio-distal), and angular (measured as deviations between the long axes of the brackets) (Fig. 4 ). This approach facilitated precise visualization and accurate measurement of bracket positioning discrepancies. The base of each bracket was used as the reference point for quantifying positional deviations. The operator (ICS), responsible for performing the measurements, underwent prior calibration to ensure measurement accuracy and reliability. This calibration process involved repeating the measurements of four randomly selected bondings after a 10-day interval, verifying the consistency and reproducibility of the results. 2.1 Statistical Analysis The sample size calculation was conducted using G*Power software, version 3.1 (Heinrich Heine University Düsseldorf, Düsseldorf, Germany), based on the findings of a clinical trial by De Oliveira et al. [ 21 ]. This trial reported a standard deviation of 0.35 mm for accessory positioning among experienced clinicians and 0.59 mm for inexperienced clinicians. A 95% power level was selected for the calculation, with an assumed detectable clinical difference of 0.50 mm. Statistical analyses and graphical representations were performed using Statistical Product and Service Solutions (SPSS®), version 23.0 for macOS (IBM Corp, Chicago, Illinois, USA). The Shapiro-Wilk test was applied to assess the normality of the sample. The distribution of bracket positioning differences was asymmetric across all measurements, with significance levels below 0.05, confirming that the sample did not follow a normal distribution. As a result, variations in virtual bracket positioning techniques, both with and without CBCT visualization, were analyzed using the Wilcoxon paired sample test across three spatial planes: vertical, horizontal, and angular. Additionally, clinical limits were set at 0.5 mm for linear dimensions and 2º for angular measurements to compare bracket positioning deviations for each tooth. Finally, the Intra-Class Correlation Coefficient (ICC) test was employed to verify the reproducibility of bracket positioning measurements performed by the single operator (ICS). A significance level of p = 0.05 was established for all analyses. 3. Results A total of 22 orthodontists participated in this study, with an average age of 35.9 years. Eleven orthodontists had less than 10 years of clinical experience, while the remaining eleven had more than 10 years of practice. Reproducibility of the measurements was confirmed using the Intra-Class Correlation Coefficient (ICC) test. The ICC values for the four bondings analyzed were as follows: 0.996 (participant 02), 0.985 (participant 07), 0.995 (participant 16), and 0.995 (participant 21), indicating excellent measurement reproducibility. Exploratory data analysis provided the medians of the differences between virtual bondings with and without CBCT visualization, a process referred to as intraoperator superimposition (IOS). Table 1 presents the medians, interquartile ranges, and maximum values for each spatial plane: vertical, horizontal, and angular. The average differences in bracket positioning between bondings by the same participant, with and without CBCT visualization (IOS), were 0.18 ± 0.23 mm in the vertical (occluso-gingival) plane and 0.10 ± 0.18 mm in the horizontal (mesio-distal) plane. For angular differences, the average deviation between the long axes of the brackets was 1.4 ± 1.9º. The interquartile ranges for the vertical plane were 0.07 mm (Q1) to 0.21 mm (Q3), and for the horizontal plane, 0.02 mm (Q1) to 0.20 mm (Q3). For angular differences, the interquartile range was 0.6º (Q1) to 2.5º (Q3). The maximum deviations between superimpositions were 1.13 mm in the vertical plane and 1.35 mm in the horizontal plane. The maximum angular deviation was 10.2º, while the minimum observed values for all planes were 0. Additionally, box plots were generated to compare bracket positioning deviations across the three spatial dimensions. These comparisons were based on clinical limits of 0.5 mm for linear dimensions and 2º for angular measurements for each tooth (Figs. 5 , 6 , and 7 ). The largest deviation in positioning was observed for tooth 33 in both the horizontal and angular dimensions. The Wilcoxon paired sample test was used to compare bracket positioning deviations in bondings with and without CBCT visualization for each operator across the three spatial dimensions. Statistically significant differences were observed for all teeth in the linear dimensions (Tables 2 and 3). For angular differences, statistically significant deviations were identified in certain teeth, including UR2, UR3, UR5, UL2, UL4, UL5, and LL1 (Table 3). 4. Discussion Several studies have evaluated the precision of indirect bonding using 3D-printed guides, comparing them to conventional indirect bonding techniques or direct bonding methods [ 15 , 22 – 27 ]. Additionally, the advent of digital planning software has enabled the integration of CBCT scans superimposed on digital models for visualizing dental roots. However, the precision of virtual indirect bonding in terms of linear and angular measurements, as performed by various orthodontists, has not been thoroughly investigated. Furthermore, despite modern CBCT devices emitting reduced radiation doses, the routine use of CBCT for all orthodontic patients remains controversial [ 28 ]. Thus, this study aimed to assess whether CBCT visualization impacts the precision of virtual indirect bonding by comparing bracket positioning performed with and without CBCT. The results rejected the null hypothesis, which proposed that CBCT would not affect bracket placement at a clinically significant level. A clinically significant difference was detected in the angular dimension for various dental elements. To evaluate reproducibility in bracket positioning, measurements were taken along standardized vertical, horizontal, and angular planes to ensure consistent evaluation, thereby enhancing measurement precision. As previously reported, methods allowing evaluators to manipulate and rotate digital models freely tend to increase bias potential [ 29 ]. In this study, a single calibrated evaluator (ICS) measured bracket positioning deviations. The reliability of these measurements was confirmed using the ICC test, which demonstrated excellent agreement across the evaluated models. These findings align with a study by Duarte et al. [ 15 ], which also confirmed intra-operator reproducibility in digital model measurements. For this study, the clinically acceptable limits for linear measurements were set at 0.5 mm, based on guidelines from the American Board of Orthodontics (ABO) [ 30 ]. The ABO indicates that discrepancies of 0.5 mm in tooth alignment and leveling can lead to negative evaluations. In this study, overall linear differences were 0.185 mm for the vertical dimension and 0.100 mm for the horizontal dimension, consistent with previously reported findings and supporting the reproducibility of the technique (Table 1). However, the literature lacks a consensus on clinically significant thresholds. Armstrong et al. suggested that deviations exceeding 0.25 mm for incisor brackets and 0.5 mm for other teeth should be considered significant [ 31 ]. In contrast, Castilla et al. argued that differences of 0.13 mm in opposing directions between adjacent brackets are clinically relevant [ 32 ]. Similarly, Koo et al. observed horizontal deviations of 0.18 mm and vertical deviations of 0.31 mm when evaluating indirect bonding [ 8 ]. Regarding angular positioning, deviations below 2º were considered clinically acceptable, based on prior studies indicating that angular deviations of less than 2º result in only a 0.5 mm difference in the marginal ridge height of average-sized molars [ 33 ]. In this study, the median angular deviation was 1.400º (Table 1). However, when individual teeth were analyzed, several results deviated from the clinically accepted 2º threshold (Table 4). Consistent with these findings, the literature indicates that achieving precise angular positioning remains challenging and prone to greater error [ 25 ]. Numerous in vitro and in vivo studies have reported superior precision in linear transfers compared to angular transfer [ 23 , 26 ]. For instance, Koo et al. [ 8 ] documented an average angular deviation of 2.438º after indirect bonding, while Bachour et al. [ 25 ] found that angular deviations using printed guides produced through a fully digital workflow frequently exceeded the 2º limit. The findings of this study further suggest that orthodontists maintained consistent bonding precision in the vertical and horizontal dimensions but exhibited variability in the angular plane. Specifically, bracket positioning deviations in the angular dimension exceeded clinically acceptable thresholds when CBCT images were utilized during virtual bonding. This observation suggests that CBCT visualization influenced angular bonding precision. These results align with the work of Oliveira et al. [ 12 ] and Lee et al. [ 34 ], who observed angular errors in bracket placement when root visualization was absent. However, these studies contrast with the findings of Nichols et al. [ 35 ], who reported consistent bracket placement during indirect bonding when CBCT was used. One notable finding of this study was the identification of the tooth with the greatest angular deviation during indirect bonding. Teeth UL3, LL3, and LR3 did not exhibit statistically significant deviations from the clinically acceptable angular values. However, tooth LL3 demonstrated the highest mean angular deviation at 3.200º (Table 4). This may be attributed to the unique anatomical characteristics of canines, including their long roots, specialized occlusal functions, and role in mutually protected occlusion, as highlighted by Balut et al. [ 36 ]. Variations in crown morphology and discrepancies between the crown-root angle can make it challenging to precisely determine tooth angulation, particularly in cases where there is a short crown relative to root length [ 36 , 37 ]. Additionally, canines often undergo more changes due to occlusal wear, which can further contribute to angular deviations, especially in posterior teeth. Supporting this, Bachour et al. [ 25 ] found that angular deviations using printed guides from a fully digital workflow often exceeded the 2º threshold. Similarly, Yilmaz et al. [ 11 ] reported an angular deviation of 5.56° in maxillary canines. While this study demonstrates that the use of CBCT in indirect digital bonding improves bracket placement precision, particularly in the angular dimension, CBCT should not be employed solely for this purpose. Despite technological advancements resulting in lower radiation doses from CBCT devices, the routine use of CBCT for all orthodontic patients remains controversial and is not recommended [ 28 ]. Instead, CBCT should be considered an additional clinical tool with specific applications. According to SEDENTEXCT guidelines, the most frequent indications for CBCT in orthodontics include retained or impacted teeth, severe craniofacial anomalies, significant facial discrepancies requiring orthodontic-surgical treatment, and TMJ malformations accompanied by clinical symptoms. CBCT may also be justified in adult patients undergoing critical tooth movements in regions with deficient buccolingual alveolar ridge thickness, provided it impacts treatment planning [ 19 ]. Therefore, specific orthodontic cases may warrant the use of CBCT imaging, as outlined in the SEDENTEXCT guidelines [ 19 ], where the added precision in bracket placement or other diagnostic advantages could provide measurable clinical benefits. A limitation of the present study is the use of a single patient’s diagnostic record for analysis. A more diverse sample with a greater variety of malocclusion types might have produced different outcomes. However, Yilmaz et al. [ 11 ] reported no significant correlation between malocclusion type and bracket positioning errors. Expanding the sample to include a variety of malocclusions would also necessitate strict adherence to SEDENTEXCT guidelines [ 19 ] regarding CBCT use in orthodontics. Consequently, most cases requiring CBCT involve impacted dental elements or surgical cases, which limits the feasibility of comprehensively assessing a broader sample. Another limitation is the absence of panoramic radiographs for comparison with results derived from bracket positioning based on root visualization from the superimposed CBCT images. This decision was made to simulate the typical workflow of digital planning services, where radiographic exams are not always mandatory. Notably, Yilmaz et al. [ 11 ] compared digital bracket placement using both CBCT and panoramic radiographs and found a statistically significant difference between the two methods, with CBCT demonstrating superior precision. Nevertheless, the absence of panoramic radiographs remains a limitation, as they could provide root visualization, which is known to offer clinical advantages. It is possible that in certain cases, panoramic radiographs alone might suffice to ensure accurate bracket positioning. 5. Conclusion Through the superimposition of digital bondings and the statistical analysis of the data, it was concluded that, when comparing the differences in bracket positioning between bondings performed by study participants with and without CBCT visualization, the average positional discrepancies remained below the clinically acceptable threshold of 0.5 mm for both the horizontal (mesio-distal) and vertical (occluso-gingival) dimensions. However, in the angular dimension, the mean deviations for teeth LL2, LL3, and LL4 exceeded the clinically acceptable limit of 2 degrees. These findings suggest that participants were able to maintain their bonding standards in the vertical, horizontal, and the majority of angular dimensions. However, the superimposition of CBCT images on digital models was found to influence the precision of virtual bonding in the angular dimension. Declarations Author Contribution I.C.S: Responsible for conceptualization, protocol development, data collection, measurements, data analysis, data interpretation, statistical analysis, and manuscript writing. D.C.K.: Contributed to conceptualization, protocol development, supervision of software measurement, data interpretation, manuscript writing, and reviewing/editing of the manuscript. B.F.G.: Involved in conceptualization, protocol development, and reviewing/editing of the manuscript. J.A.M.M.: Contributed to conceptualization, protocol development, supervision of data collection and data analysis, data interpretation, and reviewing/editing of the manuscript. Acknowledgement This study received partial funding from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) in Brazil, under Finance Code 001. We thank Dr. Ronir Raggio Luiz for his guidance on the statistical aspects of this research. The authors declare no financial conflict of interest with any of the companies whose materials were referenced in this article. Funding Declaration This study received partial funding from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) in Brazil, under Finance Code 001. Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Yıldırım K, Saglam-Aydinatay B. Comparative assessment of treatment efficacy and adverse effects during nonextraction orthodontic treatment of Class I malocclusion patients with direct and indirect bonding: A parallel randomized clinical trial. Am J Orthod Dentofac Orthop. 2018;154(1):26–e341. https://doi.org/10.1016/j.ajodo.2017.12.009 . El-Timamy AM, El-Sharaby FA, Eid FH, Mostafa YA. Three-dimensional imaging for indirect-direct bonding. 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Casko JS, Vaden JL, Kokich VG, Damone J, James RD, Cangialosi TJ, Riolo ML, Owens SE, Bills ED. Objective grading system for dental casts and panoramic radiographs. American Board of Orthodontics. Am J Orthod Dentofac Orthop. 1998;114(5). https://doi.org/10.1016/s0889-5406(98)70179-9 . 589 – 99. Armstrong D, Shen G, Petocz P, Darendeliler MA. A comparison of accuracy in bracket positioning between two techniques—localizing the centre of the clinical crown and measuring the distance from the incisal edge. Eur J Orthod. 2007;29(5):430–6. https://doi.org/10.1093/ejo/cjm037 . Castilla AE, Crowe JJ, Moses JR, Wang M, Ferracane JL, Covell DA Jr. Measurement and comparison of bracket transfer accuracy of five indirect bonding techniques. Angle Orthod. 2014;84(4):607–14. https://doi.org/10.2319/070113-484.1 . Keating AP, Knox J, Bibb R, Zhurov AI. A comparison of plaster, digital and reconstructed study model accuracy. J Orthodont. 2008;35(3):191–201. https://doi.org/10.1179/146531207225022626 . Lee RJ, Ko J, Park J, Pi S, Devgon D, Nelson G, Hatcher D, Oberoi S. Accuracy and reliability of the expected root position setup on clinical decision making of root position at midtreatment. Am J Orthod Dentofac Orthop. 2019;156(4):566–73. https://doi.org/10.1016/j.ajodo.2019.03.018 . Nichols DA, Gardner G, Carballeyra AD. Reproducibility of bracket positioning in the indirect bonding technique. Am J Orthod Dentofac Orthop. 2013;144(5):770–6. https://doi.org/10.1016/j.ajodo.2013.04.023 . Balut N, Klapper L, Sandrik J, Bowman D. Variations in bracket placement in the preadjusted orthodontic appliance. Am J Orthod Dentofac Orthop. 1992;102(1):62–7. https://doi.org/10.1016/0889-5406(92)70015-3 . Carlsson R, Ro A. Crown-root angles of upper central incisors. Am J Orthod. 1973;64(2):147–54. https://doi.org/10.1016/s0002-9416(73)90306-0 . Tables Table 1. Overall assessment of bracket positioning differences across each spatial plane. Median Q1 Q3 Min Max V_IOS (mm) 0.185 0.078 0.210 0.000 1.130 H_IOS (mm) 0.100 0.020 0.200 0.000 1.350 A_IOS ( o ) 1.400 0.600 2.500 0.000 10.200 Legend: V = vertical plane. H = horizontal plane. A = angulation. IOS = intraoperator superimposition. Q1 = 25th percentile. Q3 = 75th percentile. Table 2. Descriptive statistics of the indirect bonding precision for vertical values among various dental elements. Data is provided in millimeters (mm). Comparisons are made with the clinical reference value of 0.5mm. Median Q1 Q3 Min Max p-value UR1 0.200 0.075 0.313 0.000 0.960 0.001* UR2 0.110 0.010 0.193 0.000 0.530 0.000* UR3 0.175 0.030 0.300 0.000 1.010 0.007* UR4 0.200 0.100 0.540 0.000 0.880 0.003* UR5 0.120 0.068 0.415 0.000 0.590 0.000* UL1 0.160 0.008 0.303 0.000 0.920 0.000* UL2 0.140 0.110 0.290 0.030 0.640 0.000* UL3 0.200 0.108 0.480 0.030 0.810 0.001* UL4 0.250 0.095 0.403 0.000 0.630 0.000* UL5 0.210 0.090 0.383 0.000 0.840 0.001* LL1 0.100 0.010 0.303 0.000 0.610 0.000* LL2 0.190 0.190 0.190 0.190 0.190 0.000* LL3 0.200 0.088 0.378 0.010 1.130 0.002* LL4 0.190 0.078 0.200 0.000 0.600 0.000* LL5 0.125 0.025 0.298 0.000 0.450 0.000* LR1 0.110 0.068 0.333 0.000 0.800 0.000* LR2 0.195 0.068 0.400 0.000 0.610 0.000* LR3 0.060 0.028 0.348 0.000 0.710 0.000* LR4 0.190 0.025 0.233 0.000 0.630 0.000* LR5 0.210 0.163 0.360 0.000 0.890 0.001* *Significant at p < 0.05. P-value for precision, Wilcoxon test. Q1= 25 th percentile, Q3= 75 th percentile, Min= Minimum, Max= Maximum. Table 3. Descriptive statistics of the indirect bonding precision for horizontal values among various dental elements. Data is provided in millimetres (mm). Comparisons are made with the clinical reference value of 0.5mm. Median Q1 Q3 Min Max p-value UR1 0.110 0.018 0.203 0.000 0.600 0.000* UR2 0.045 0.010 0.115 0.000 0.330 0.000* UR3 0.080 0.043 0.125 0.000 0.370 0.000* UR4 0.115 0.028 0.208 0.000 0.700 0.000* UR5 0.105 0.000 0.180 0.000 0.290 0.000* UL1 0.055 0.000 0.158 0.000 0.380 0.000* UL2 0.030 0.000 0.080 0.000 0.210 0.000* UL3 0.105 0.068 0.210 0.020 0.670 0.000* UL4 0.100 0.028 0.120 0.000 0.250 0.000* UL5 0.110 0.020 0.173 0.000 0.260 0.000* LL1 0.085 0.010 0.170 0.000 0.330 0.000* LL2 0.110 0.058 0.220 0.000 0.480 0.000* LL3 0.245 0.128 0.478 0.010 0.750 0.003* LL4 0.100 0.028 0.228 0.000 0.480 0.000* LL5 0.105 0.035 0.208 0.000 1.350 0.001* LR1 0.125 0.020 0.175 0.000 0.400 0.000* LR2 0.125 0.040 0.248 0.010 0.550 0.000* LR3 0.110 0.018 0.218 0.000 0.470 0.000* LR4 0.140 0.068 0.288 0.000 0.590 0.000* LR5 0.105 0.035 0.303 0.000 0.410 0.000* *Significant at p < 0.05. P-value for precision, Wilcoxon test. Q1= 25 th percentile, Q3= 75 th percentile, Min= Minimum, Max= Maximum. Table 4. Descriptive statistics of the indirect bonding precision for angulation values among various dental elements. Data is provided in degrees ( o ). Comparisons are made with the clinical reference value of 2 o . Median Q1 Q3 Min Max p-value UR1 1.650 0.300 2.825 0.000 7.900 0.554 UR2 1.100 0.200 1.525 0.000 5.000 0.003* UR3 1.050 0.400 1.825 0.000 5.700 0.006* UR4 1.450 0.875 2.325 0.000 7.300 0.157 UR5 0.850 0.375 1.500 0.000 4.100 0.003* UL1 1.300 0.800 2.425 0.000 6.700 0.217 UL2 1.150 0.700 2.200 0.000 4.100 0.041* UL3 1.800 0.200 2.850 0.000 10.200 0.306 UL4 1.300 0.600 2.125 0.000 3.800 0.007* UL5 1.350 0.300 2.125 0.000 4.400 0.026* LL1 1.250 0.150 2.425 0.100 3.900 0.025* LL2 2.050 0.950 3.625 0.000 7.500 0.476 LL3 3.200 1.350 6.475 0.000 9.100 0.012 LL4 1.600 0.450 2.925 0.100 7.500 0.475 LL5 1.400 0.500 3.650 0.000 8.000 0.903 LR1 1.450 0.775 2.050 0.000 4.500 0.024* LR2 1.350 0.850 2.725 0.000 8.800 0.269 LR3 1.500 0.650 3.100 0.000 6.400 0.495 LR4 1.500 0.575 3.700 0.000 6.200 0.673 LR5 0.105 0.035 0.303 0.000 0.410 0.661 *Significant at p < 0.05. P-value for precision, Wilcoxon test. Q1= 25 th percentile, Q3= 75 th percentile, Min= Minimum, Max= Maximum. Additional Declarations No competing interests reported. Supplementary Files Highlightsmanuscript.docx 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6976645","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":487729999,"identity":"6ac986d2-cff6-46e0-ad3b-4335065b867c","order_by":0,"name":"Ivanise Cardoso da Silva","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ivanise","middleName":"Cardoso da","lastName":"Silva","suffix":""},{"id":487730000,"identity":"fa99a39d-0403-4719-83be-6fc589ce20f3","order_by":1,"name":"Deise Caldas Kühlman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYFAC5gYJIMnYwAwkPwAxGztBLYwILYwzQFqYidYCspAHbC0BDfztBxtv/PjDINvPzv7wsc2vbfJ8zAyMHz7m4NYicSax2bK3jcF4ZjOPsXFu323DNmYGZsmZ23BrMWBIbJPgbWBI3HCYh006t+c2I1ALGzMvPi38D9sk//xhSNx/mP35b8ue2/aEtUgktknzsAFtYWYwY2b4cTuRoBaJGw+brWXbJIxnHOYxluxtuJ3cxszYjNcv/P3JB2+++WMj299//OGHH39u285vbz744SMeLTDLIBRjG5hsIKgeCfwhRfEoGAWjYBSMFAAA1KpN382N6wkAAAAASUVORK5CYII=","orcid":"","institution":"Rio de Janeiro State University","correspondingAuthor":true,"prefix":"","firstName":"Deise","middleName":"Caldas","lastName":"Kühlman","suffix":""},{"id":487730001,"identity":"5ee60b22-03ff-4fee-b640-c3ff9239e911","order_by":2,"name":"Bruno Frazão Gribel","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Bruno","middleName":"Frazão","lastName":"Gribel","suffix":""},{"id":487730003,"identity":"f285fdb5-031f-4a06-b841-83b1ae462fbb","order_by":3,"name":"José Augusto Mendes Miguel","email":"","orcid":"","institution":"Rio de Janeiro State University","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"Augusto Mendes","lastName":"Miguel","suffix":""}],"badges":[],"createdAt":"2025-06-25 16:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6976645/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6976645/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87335003,"identity":"44bc03dd-ccff-4ce8-817b-f48e39db21d3","added_by":"auto","created_at":"2025-07-22 20:22:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":10131989,"visible":true,"origin":"","legend":"\u003cp\u003eSelected images of a malocclusion for virtual indirect bonding.\u003cstrong\u003e A\u003c/strong\u003e Right lateral view. \u003cstrong\u003eB \u003c/strong\u003eFrontal view. \u003cstrong\u003eC\u003c/strong\u003e Left lateral view. \u003cstrong\u003eD\u003c/strong\u003e Upper occlusal view. \u003cstrong\u003eE \u003c/strong\u003eLower occlusal view. \u003cstrong\u003eF\u003c/strong\u003e Tomography overlaid on digital models.\u003c/p\u003e","description":"","filename":"Figure1A.png","url":"https://assets-eu.researchsquare.com/files/rs-6976645/v1/831e1a6accbbb56b0af2fec9.png"},{"id":87335002,"identity":"5bd038ba-1339-412d-ae24-4a58131cbd3e","added_by":"auto","created_at":"2025-07-22 20:22:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4981137,"visible":true,"origin":"","legend":"\u003cp\u003eSoftware interface during virtual bracket positioning. \u003cstrong\u003eA \u003c/strong\u003eVirtual bonding without tomographic image visualization. \u003cstrong\u003eB \u003c/strong\u003eVirtual bonding with tomographic image visualization.\u003c/p\u003e","description":"","filename":"Figure2A.png","url":"https://assets-eu.researchsquare.com/files/rs-6976645/v1/7a4d4046b73a2de4a7d1371c.png"},{"id":87334620,"identity":"b3b5af9c-4bdd-4b11-bc9f-0dc8625fec07","added_by":"auto","created_at":"2025-07-22 20:14:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":7032565,"visible":true,"origin":"","legend":"\u003cp\u003eModel superimposition. \u003cstrong\u003eA\u003c/strong\u003e Set of models from participant number 01, chosen for intra-operator superimposition (IOS). \u003cstrong\u003eB\u003c/strong\u003e Frontal view displaying the overlaid models.\u003c/p\u003e","description":"","filename":"Figure3A.png","url":"https://assets-eu.researchsquare.com/files/rs-6976645/v1/25e2ffa3316e69f399602bda.png"},{"id":87335006,"identity":"90097082-229b-46b1-8585-9b41d91031b3","added_by":"auto","created_at":"2025-07-22 20:22:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":10799047,"visible":true,"origin":"","legend":"\u003cp\u003eMeasurements conducted to assess the bracket position differences. \u003cstrong\u003eA\u003c/strong\u003e Vertical direction. \u003cstrong\u003eB\u003c/strong\u003e Horizontal direction.\u003cstrong\u003e C \u003c/strong\u003eAngulation, which represents the difference between the long axes of the brackets.\u003c/p\u003e","description":"","filename":"Figure4A.png","url":"https://assets-eu.researchsquare.com/files/rs-6976645/v1/58ccc8ee47561957a750c32b.png"},{"id":87334016,"identity":"02badf8d-1a0c-42d2-bcdd-d0f38a602c05","added_by":"auto","created_at":"2025-07-22 20:06:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":87454,"visible":true,"origin":"","legend":"\u003cp\u003eDescriptive statistics for each dental element. The horizontal line set at 0.5mm represents the clinical reference values.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6976645/v1/656754786cb8a7a430ef1108.png"},{"id":87334017,"identity":"d7ab40b1-41f8-4da0-9e4e-27a0a2c3435b","added_by":"auto","created_at":"2025-07-22 20:06:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":94111,"visible":true,"origin":"","legend":"\u003cp\u003eDescriptive statistics for each dental element. The horizontal line set at 0.5mm represents the clinical reference values.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6976645/v1/30e3cdaf3681247e62ec1ddf.png"},{"id":87334618,"identity":"06872b72-da95-41a0-8667-af585f70297c","added_by":"auto","created_at":"2025-07-22 20:14:43","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":95092,"visible":true,"origin":"","legend":"\u003cp\u003eDescriptive statistics for each dental element. The horizontal line set at 2 degrees represents the clinical reference values.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-6976645/v1/63d01c76811e3292c5752524.png"},{"id":97140219,"identity":"d34bc407-6b93-4326-a902-0bcf93018686","added_by":"auto","created_at":"2025-12-01 10:04:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":44695625,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6976645/v1/e3c44509-63f5-4a46-a663-8ed1a6541d8e.pdf"},{"id":87334032,"identity":"a4189845-8980-446b-a2b7-f7a920aeb112","added_by":"auto","created_at":"2025-07-22 20:06:43","extension":"docx","order_by":18,"title":"","display":"","copyAsset":false,"role":"supplement","size":15252,"visible":true,"origin":"","legend":"","description":"","filename":"Highlightsmanuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-6976645/v1/40fe75713e90725f37639fa8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePrecision of the Indirect Bonding Method Using CAD/CAM Technology and CBCT Image Superimposition\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIndirect bonding techniques have gained significant popularity in orthodontics due to their two-phase process, encompassing both laboratory and clinical stages [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Key advantages of this approach include enhanced precision in bracket placement and a reduction in chair time during clinical appointments [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. While precision in bracket positioning is often highlighted as a primary advantage, studies comparing direct and indirect bonding methods, both in vitro and in vivo, have yielded inconsistent results [\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8 CR9\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe emergence of digital dentistry has introduced advancements in orthodontic diagnostics and treatment planning [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Computer-aided design/computer-aided manufacturing (CAD/CAM) technology enables virtual setups, virtual bracket positioning, and the fabrication of customized bonding guides using three-dimensional (3D) printers. The implementation of CAD/CAM-based indirect bonding protocols aims to minimize human error in bracket placement. Despite this, several studies have raised concerns regarding the precision of digital indirect bonding during both the planning and laboratory phases. These concerns may stem from limitations inherent to the software, the learning curve faced by practitioners, or errors during bracket transfer [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eConversely, research has demonstrated that CAD/CAM-based indirect bonding significantly enhances bracket positioning precision, contributing to reduced treatment times [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Duarte et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] found that indirect bonding techniques using digital guides when designed by an experienced practitioner, can be reproducibly applied regardless of the clinician's experience. Nevertheless, no studies have investigated the reproducibility of this technique when applied by different orthodontists.\u003c/p\u003e\u003cp\u003eCone-beam computed tomography (CBCT) has become a valuable tool in orthodontic diagnosis and treatment planning, though it introduces greater radiation exposure and higher costs compared to conventional panoramic and cephalometric radiographs [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Additionally, distinct guidelines on the use of CBCT in orthodontics have been published for North America and Europe. The North American guidelines were developed by the American Academy of Oral and Maxillofacial Radiology (AAOMR) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], while the European recommendations are part of the SEDENTEXCT project [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The primary difference between the North American and European recommendations can be attributed to the distinct criteria used. The North American guidelines were based on the most frequent applications of CBCT reported in the literature. In contrast, the SEDENTEXCT guidelines were strictly based on the presence of high levels of evidence regarding CBCT's efficacy [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Nevertheless, both sets of guidelines recommend the use of CBCT primarily for complex cases, such as impacted teeth, critical dental movements in periodontally compromised patients, and pre-surgical orthodontic assessments [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMoreover, in comparison to traditional two-dimensional imaging, CBCT allows for more precise evaluations of both crowns and roots. When applied to virtual bracket placement on digital models, CBCT superimposition facilitates the visualization of root inclinations, thus it may enhance the precision of appliance placement and promote improved root parallelism after treatment [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite these advantages, no studies have examined the precision of CAD/CAM-based indirect bonding in comparison to virtual bracket placement involving CBCT superimposition.\u003c/p\u003e\u003cp\u003eThis study, therefore, aims to evaluate the precision of virtual bracket placement by superimposing CBCT images onto digital models, with the bonding plans executed by various orthodontists. The null hypothesis proposes that no significant differences exist in linear or angular deviations between bonding techniques with and without CBCT integration.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e This study was initiated following approval by the Research Ethics Committee of Pedro Ernesto University Hospital, at the State University of Rio de Janeiro (UERJ) (Approval No. 4.950.248).\u003c/p\u003e\u003cp\u003eA total of 22 orthodontists participated in the digital planning procedures for bracket positioning. Participants were recruited through the Brazilian Association of Orthodontics (ABOR) and the Orthodontic Clinic at UERJ. Inclusion criteria required participants to hold a certificate in orthodontics, maintain active registration, be currently practicing fixed orthodontic treatments, and have at least three years of experience. All participants provided written informed consent and completed a questionnaire detailing their years of clinical practice.\u003c/p\u003e\u003cp\u003eThe diagnostic records of a single patient were used for this study. These included stereolithography (STL) files representing digital dental models of the upper and lower arches and Digital Imaging and Communications in Medicine (DICOM) files containing CBCT scans (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The patient signed an informed consent form agreeing to participate in the study and to the use of their diagnostic records for research purposes. This study was conducted in accordance with the principles of the Declaration of Helsinki. The CBCT images were required for superimposition onto the digital models, and it was clinically indicated following the SEDENTEXCT guidelines for orthodontic treatment planning [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The selected patient was a Caucasian female with a Class II Division 1 incisor relationship, a mild Class II skeletal base, and average vertical proportions. His malocclusion was complicated by moderate crowding in the upper arch, severe crowding in the lower arch, increased overjet, and a 2mm overbite. The buccal segment relationships are presented on both sides with a class I molar relationship.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe patient\u0026rsquo;s STL and DICOM files were imported into OrthoAnalyzer\u0026trade; software (3Shape A/S, Copenhagen, Denmark), licensed from Compass 3D\u0026trade; (Compass 3D, Belo Horizonte, Minas Gerais, Brazil), a laboratory specializing in digital orthodontic services. The software was accessed remotely throughout the study using TeamViewer\u0026trade; version 15.42.7 (TeamViewer AG, G\u0026ouml;ppingen, Germany). The superimposition of CBCT images onto the digital dental models was performed using three corresponding points identified in each image. The software\u0026rsquo;s best-fit algorithm aligned these points, producing a combined model that displayed clinical crowns digitally and revealed dental roots and surrounding bony structures via tomography (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In total, 44 models were generated for the digital indirect bonding evaluation: 22 models displaying only clinical crowns and 22 models incorporating root visualizations.\u003c/p\u003e\u003cp\u003eMoreover, Roth prescription 0.022x0.025\u0026rdquo; slot Mini Master\u0026trade; brackets (American Orthodontics, Sheboygan, WI, USA) were selected within OrthoAnalyzer\u0026trade; software for placement on teeth from the second premolar to the second premolar in both arches. Molars were excluded from bonding due to their limited representation in the software\u0026rsquo;s virtual library.\u003c/p\u003e\u003cp\u003eBefore the digital bracket positioning procedure, participants received comprehensive written and oral instructions on software interaction and effective command execution. The main bracket positioning instructions were as follows: (1) Adjustments were performed during remote sessions via TeamViewer\u0026trade;, where participants observed the OrthoAnalyzer\u0026trade; interface; (2) Bracket adjustments were performed exclusively by a single experience operator (ICS) on voice commands from participants; (3) Brackets were initially positioned automatically by the software at the midpoint of the facial axis of the clinical crown on the buccal surface of each tooth, serving as the default starting point; (4) Brackets were then adjusted along three spatial planes\u0026mdash;vertical, horizontal, and angular\u0026mdash;using the mouse or keyboard controls on the operator's MacBook Air\u0026trade; (macOS Sonoma version 14, Apple Inc., Cupertino, CA, USA). Adjustments were measured in millimeters for magnitude and degrees for angular deviations.\u003c/p\u003e\u003cp\u003eIn the first phase, participants planned bracket positioning while visualizing only the digital models of the clinical crowns (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). After a minimum interval of 15 days, participants repeated the bonding process, this time incorporating CBCT superimposition for visualization of both crowns and roots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Participants had access to CBCT data at any time during this phase. Procedures were standardized and conducted in the same location, under consistent lighting conditions, using a MacBook Air\u0026trade;.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFollowing the virtual bondings, model comparisons\u0026mdash;with and without CBCT integration\u0026mdash;were conducted for each participant. This process, termed intra-operator superimposition (IOS), adhered to OrthoAnalyzer\u0026trade; protocols. The \"Model Comparison\" feature was used to align dental models with and without root visualizations, including the positioned brackets, via the software's best-fit algorithm. This ensured alignment based on stable anatomical regions, eliminating the need for manual point selection (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Once aligned, the \"Overlay and Display\" option was activated to merge the models into a single visualization, enabling direct comparison of bracket positional changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). This functionality provided a detailed analysis by highlighting positional discrepancies within the software interface.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eMeasurements of positional discrepancies were conducted following superimposition to quantify differences in bracket positioning. Positional differences were analyzed by generating automated planes in OrthoAnalyzer\u0026trade; software, which created sagittal, axial, and coronal sections based on the long axis and the mesial, distal, buccal, and lingual surfaces of each tooth. This methodology enabled the evaluation of bracket positioning across three spatial planes: vertical (occluso-gingival), horizontal (mesio-distal), and angular (measured as deviations between the long axes of the brackets) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This approach facilitated precise visualization and accurate measurement of bracket positioning discrepancies. The base of each bracket was used as the reference point for quantifying positional deviations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe operator (ICS), responsible for performing the measurements, underwent prior calibration to ensure measurement accuracy and reliability. This calibration process involved repeating the measurements of four randomly selected bondings after a 10-day interval, verifying the consistency and reproducibility of the results.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Statistical Analysis\u003c/h2\u003e\u003cp\u003eThe sample size calculation was conducted using G*Power software, version 3.1 (Heinrich Heine University D\u0026uuml;sseldorf, D\u0026uuml;sseldorf, Germany), based on the findings of a clinical trial by De Oliveira et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This trial reported a standard deviation of 0.35 mm for accessory positioning among experienced clinicians and 0.59 mm for inexperienced clinicians. A 95% power level was selected for the calculation, with an assumed detectable clinical difference of 0.50 mm.\u003c/p\u003e\u003cp\u003eStatistical analyses and graphical representations were performed using Statistical Product and Service Solutions (SPSS\u0026reg;), version 23.0 for macOS (IBM Corp, Chicago, Illinois, USA).\u003c/p\u003e\u003cp\u003eThe Shapiro-Wilk test was applied to assess the normality of the sample. The distribution of bracket positioning differences was asymmetric across all measurements, with significance levels below 0.05, confirming that the sample did not follow a normal distribution. As a result, variations in virtual bracket positioning techniques, both with and without CBCT visualization, were analyzed using the Wilcoxon paired sample test across three spatial planes: vertical, horizontal, and angular. Additionally, clinical limits were set at 0.5 mm for linear dimensions and 2\u0026ordm; for angular measurements to compare bracket positioning deviations for each tooth.\u003c/p\u003e\u003cp\u003eFinally, the Intra-Class Correlation Coefficient (ICC) test was employed to verify the reproducibility of bracket positioning measurements performed by the single operator (ICS). A significance level of p\u0026thinsp;=\u0026thinsp;0.05 was established for all analyses.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eA total of 22 orthodontists participated in this study, with an average age of 35.9 years. Eleven orthodontists had less than 10 years of clinical experience, while the remaining eleven had more than 10 years of practice.\u003c/p\u003e\u003cp\u003eReproducibility of the measurements was confirmed using the Intra-Class Correlation Coefficient (ICC) test. The ICC values for the four bondings analyzed were as follows: 0.996 (participant 02), 0.985 (participant 07), 0.995 (participant 16), and 0.995 (participant 21), indicating excellent measurement reproducibility.\u003c/p\u003e\u003cp\u003eExploratory data analysis provided the medians of the differences between virtual bondings with and without CBCT visualization, a process referred to as intraoperator superimposition (IOS). Table\u0026nbsp;1 presents the medians, interquartile ranges, and maximum values for each spatial plane: vertical, horizontal, and angular. The average differences in bracket positioning between bondings by the same participant, with and without CBCT visualization (IOS), were 0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23 mm in the vertical (occluso-gingival) plane and 0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18 mm in the horizontal (mesio-distal) plane. For angular differences, the average deviation between the long axes of the brackets was 1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u0026ordm;.\u003c/p\u003e\u003cp\u003eThe interquartile ranges for the vertical plane were 0.07 mm (Q1) to 0.21 mm (Q3), and for the horizontal plane, 0.02 mm (Q1) to 0.20 mm (Q3). For angular differences, the interquartile range was 0.6\u0026ordm; (Q1) to 2.5\u0026ordm; (Q3). The maximum deviations between superimpositions were 1.13 mm in the vertical plane and 1.35 mm in the horizontal plane. The maximum angular deviation was 10.2\u0026ordm;, while the minimum observed values for all planes were 0. Additionally, box plots were generated to compare bracket positioning deviations across the three spatial dimensions. These comparisons were based on clinical limits of 0.5 mm for linear dimensions and 2\u0026ordm; for angular measurements for each tooth (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The largest deviation in positioning was observed for tooth 33 in both the horizontal and angular dimensions.\u003c/p\u003e\u003cp\u003eThe Wilcoxon paired sample test was used to compare bracket positioning deviations in bondings with and without CBCT visualization for each operator across the three spatial dimensions. Statistically significant differences were observed for all teeth in the linear dimensions (Tables\u0026nbsp;2 and 3). For angular differences, statistically significant deviations were identified in certain teeth, including UR2, UR3, UR5, UL2, UL4, UL5, and LL1 (Table\u0026nbsp;3).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eSeveral studies have evaluated the precision of indirect bonding using 3D-printed guides, comparing them to conventional indirect bonding techniques or direct bonding methods [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR23 CR24 CR25 CR26\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Additionally, the advent of digital planning software has enabled the integration of CBCT scans superimposed on digital models for visualizing dental roots. However, the precision of virtual indirect bonding in terms of linear and angular measurements, as performed by various orthodontists, has not been thoroughly investigated. Furthermore, despite modern CBCT devices emitting reduced radiation doses, the routine use of CBCT for all orthodontic patients remains controversial [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThus, this study aimed to assess whether CBCT visualization impacts the precision of virtual indirect bonding by comparing bracket positioning performed with and without CBCT. The results rejected the null hypothesis, which proposed that CBCT would not affect bracket placement at a clinically significant level. A clinically significant difference was detected in the angular dimension for various dental elements.\u003c/p\u003e\u003cp\u003eTo evaluate reproducibility in bracket positioning, measurements were taken along standardized vertical, horizontal, and angular planes to ensure consistent evaluation, thereby enhancing measurement precision. As previously reported, methods allowing evaluators to manipulate and rotate digital models freely tend to increase bias potential [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In this study, a single calibrated evaluator (ICS) measured bracket positioning deviations. The reliability of these measurements was confirmed using the ICC test, which demonstrated excellent agreement across the evaluated models. These findings align with a study by Duarte et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], which also confirmed intra-operator reproducibility in digital model measurements.\u003c/p\u003e\u003cp\u003eFor this study, the clinically acceptable limits for linear measurements were set at 0.5 mm, based on guidelines from the American Board of Orthodontics (ABO) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The ABO indicates that discrepancies of 0.5 mm in tooth alignment and leveling can lead to negative evaluations. In this study, overall linear differences were 0.185 mm for the vertical dimension and 0.100 mm for the horizontal dimension, consistent with previously reported findings and supporting the reproducibility of the technique (Table\u0026nbsp;1). However, the literature lacks a consensus on clinically significant thresholds. Armstrong et al. suggested that deviations exceeding 0.25 mm for incisor brackets and 0.5 mm for other teeth should be considered significant [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In contrast, Castilla et al. argued that differences of 0.13 mm in opposing directions between adjacent brackets are clinically relevant [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Similarly, Koo et al. observed horizontal deviations of 0.18 mm and vertical deviations of 0.31 mm when evaluating indirect bonding [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRegarding angular positioning, deviations below 2\u0026ordm; were considered clinically acceptable, based on prior studies indicating that angular deviations of less than 2\u0026ordm; result in only a 0.5 mm difference in the marginal ridge height of average-sized molars [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In this study, the median angular deviation was 1.400\u0026ordm; (Table\u0026nbsp;1). However, when individual teeth were analyzed, several results deviated from the clinically accepted 2\u0026ordm; threshold (Table\u0026nbsp;4). Consistent with these findings, the literature indicates that achieving precise angular positioning remains challenging and prone to greater error [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Numerous in vitro and in vivo studies have reported superior precision in linear transfers compared to angular transfer [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. For instance, Koo et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] documented an average angular deviation of 2.438\u0026ordm; after indirect bonding, while Bachour et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] found that angular deviations using printed guides produced through a fully digital workflow frequently exceeded the 2\u0026ordm; limit.\u003c/p\u003e\u003cp\u003eThe findings of this study further suggest that orthodontists maintained consistent bonding precision in the vertical and horizontal dimensions but exhibited variability in the angular plane. Specifically, bracket positioning deviations in the angular dimension exceeded clinically acceptable thresholds when CBCT images were utilized during virtual bonding. This observation suggests that CBCT visualization influenced angular bonding precision.\u003c/p\u003e\u003cp\u003eThese results align with the work of Oliveira et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and Lee et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], who observed angular errors in bracket placement when root visualization was absent. However, these studies contrast with the findings of Nichols et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], who reported consistent bracket placement during indirect bonding when CBCT was used.\u003c/p\u003e\u003cp\u003eOne notable finding of this study was the identification of the tooth with the greatest angular deviation during indirect bonding. Teeth UL3, LL3, and LR3 did not exhibit statistically significant deviations from the clinically acceptable angular values. However, tooth LL3 demonstrated the highest mean angular deviation at 3.200\u0026ordm; (Table\u0026nbsp;4). This may be attributed to the unique anatomical characteristics of canines, including their long roots, specialized occlusal functions, and role in mutually protected occlusion, as highlighted by Balut et al. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Variations in crown morphology and discrepancies between the crown-root angle can make it challenging to precisely determine tooth angulation, particularly in cases where there is a short crown relative to root length [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Additionally, canines often undergo more changes due to occlusal wear, which can further contribute to angular deviations, especially in posterior teeth. Supporting this, Bachour et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] found that angular deviations using printed guides from a fully digital workflow often exceeded the 2\u0026ordm; threshold. Similarly, Yilmaz et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] reported an angular deviation of 5.56\u0026deg; in maxillary canines.\u003c/p\u003e\u003cp\u003eWhile this study demonstrates that the use of CBCT in indirect digital bonding improves bracket placement precision, particularly in the angular dimension, CBCT should not be employed solely for this purpose. Despite technological advancements resulting in lower radiation doses from CBCT devices, the routine use of CBCT for all orthodontic patients remains controversial and is not recommended [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Instead, CBCT should be considered an additional clinical tool with specific applications. According to SEDENTEXCT guidelines, the most frequent indications for CBCT in orthodontics include retained or impacted teeth, severe craniofacial anomalies, significant facial discrepancies requiring orthodontic-surgical treatment, and TMJ malformations accompanied by clinical symptoms. CBCT may also be justified in adult patients undergoing critical tooth movements in regions with deficient buccolingual alveolar ridge thickness, provided it impacts treatment planning [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Therefore, specific orthodontic cases may warrant the use of CBCT imaging, as outlined in the SEDENTEXCT guidelines [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], where the added precision in bracket placement or other diagnostic advantages could provide measurable clinical benefits.\u003c/p\u003e\u003cp\u003eA limitation of the present study is the use of a single patient\u0026rsquo;s diagnostic record for analysis. A more diverse sample with a greater variety of malocclusion types might have produced different outcomes. However, Yilmaz et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] reported no significant correlation between malocclusion type and bracket positioning errors. Expanding the sample to include a variety of malocclusions would also necessitate strict adherence to SEDENTEXCT guidelines [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] regarding CBCT use in orthodontics. Consequently, most cases requiring CBCT involve impacted dental elements or surgical cases, which limits the feasibility of comprehensively assessing a broader sample.\u003c/p\u003e\u003cp\u003eAnother limitation is the absence of panoramic radiographs for comparison with results derived from bracket positioning based on root visualization from the superimposed CBCT images. This decision was made to simulate the typical workflow of digital planning services, where radiographic exams are not always mandatory. Notably, Yilmaz et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] compared digital bracket placement using both CBCT and panoramic radiographs and found a statistically significant difference between the two methods, with CBCT demonstrating superior precision. Nevertheless, the absence of panoramic radiographs remains a limitation, as they could provide root visualization, which is known to offer clinical advantages. It is possible that in certain cases, panoramic radiographs alone might suffice to ensure accurate bracket positioning.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThrough the superimposition of digital bondings and the statistical analysis of the data, it was concluded that, when comparing the differences in bracket positioning between bondings performed by study participants with and without CBCT visualization, the average positional discrepancies remained below the clinically acceptable threshold of 0.5 mm for both the horizontal (mesio-distal) and vertical (occluso-gingival) dimensions. However, in the angular dimension, the mean deviations for teeth LL2, LL3, and LL4 exceeded the clinically acceptable limit of 2 degrees.\u003c/p\u003e\u003cp\u003eThese findings suggest that participants were able to maintain their bonding standards in the vertical, horizontal, and the majority of angular dimensions. However, the superimposition of CBCT images on digital models was found to influence the precision of virtual bonding in the angular dimension.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eI.C.S: Responsible for conceptualization, protocol development, data collection, measurements, data analysis, data interpretation, statistical analysis, and manuscript writing. D.C.K.: Contributed to conceptualization, protocol development, supervision of software measurement, data interpretation, manuscript writing, and reviewing/editing of the manuscript. B.F.G.: Involved in conceptualization, protocol development, and reviewing/editing of the manuscript. J.A.M.M.: Contributed to conceptualization, protocol development, supervision of data collection and data analysis, data interpretation, and reviewing/editing of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis study received partial funding from the Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior (CAPES) in Brazil, under Finance Code 001. We thank Dr. Ronir Raggio Luiz for his guidance on the statistical aspects of this research. The authors declare no financial conflict of interest with any of the companies whose materials were referenced in this article.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFunding Declaration\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study received partial funding from the Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior (CAPES) in Brazil, under Finance Code 001.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYıldırım K, Saglam-Aydinatay B. 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Crown-root angles of upper central incisors. Am J Orthod. 1973;64(2):147\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s0002-9416(73)90306-0\u003c/span\u003e\u003cspan address=\"10.1016/s0002-9416(73)90306-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Overall assessment of bracket positioning differences across each spatial plane.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\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 style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eV_IOS (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e1.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH_IOS (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e1.350\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eA_IOS (\u003csup\u003eo\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e1.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e2.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e10.200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLegend: V = vertical plane. H = horizontal plane. A = angulation. IOS = intraoperator superimposition. Q1 = 25th percentile. Q3 = 75th percentile.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Descriptive statistics of the indirect bonding precision for vertical values among various dental elements. Data is provided in millimeters (mm). Comparisons are made with the clinical reference value of 0.5mm. \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"573\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.338%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1629%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUR1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.338%;\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1629%;\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUR2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.338%;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1629%;\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUR3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.338%;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1629%;\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e1.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.007*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUR4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.338%;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1629%;\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.003*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUR5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.338%;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1629%;\"\u003e\n \u003cp\u003e0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUL1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.338%;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1629%;\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUL2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.338%;\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1629%;\"\u003e\n \u003cp\u003e0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUL3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.338%;\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1629%;\"\u003e\n \u003cp\u003e0.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUL4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.338%;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1629%;\"\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUL5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.338%;\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1629%;\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLL1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.338%;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1629%;\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLL2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.338%;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1629%;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLL3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.338%;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1629%;\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e1.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLL4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.338%;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1629%;\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLL5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.338%;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1629%;\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLR1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.338%;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1629%;\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLR2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.338%;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1629%;\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLR3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.338%;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1629%;\"\u003e\n \u003cp\u003e0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLR4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.338%;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1629%;\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLR5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2592%;\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.338%;\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1629%;\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6602%;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Significant at \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eP-value for precision, Wilcoxon test.\u003c/p\u003e\n\u003cp\u003eQ1= 25\u003csup\u003eth\u0026nbsp;\u003c/sup\u003epercentile, Q3= 75\u003csup\u003eth\u0026nbsp;\u003c/sup\u003epercentile, Min= Minimum, Max= Maximum. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Descriptive statistics of the indirect bonding precision for horizontal values among various dental elements. Data is provided in millimetres (mm). Comparisons are made with the clinical reference value of 0.5mm.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUR1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUR2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUR3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUR4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUR5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUL1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUL2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUL3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUL4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUL5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLL1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLL2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLL3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.003*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLL4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLL5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLR1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLR2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLR3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLR4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLR5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Significant at \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eP-value for precision, Wilcoxon test.\u003c/p\u003e\n\u003cp\u003eQ1= 25\u003csup\u003eth\u0026nbsp;\u003c/sup\u003epercentile, Q3= 75\u003csup\u003eth\u0026nbsp;\u003c/sup\u003epercentile, Min= Minimum, Max= Maximum. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Descriptive statistics of the indirect bonding precision for angulation values among various dental elements. Data is provided in degrees (\u003csup\u003eo\u003c/sup\u003e). Comparisons are made with the clinical reference value of 2\u003csup\u003eo\u003c/sup\u003e.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUR1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e7.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUR2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e5.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.003*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUR3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e5.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.006*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUR4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e7.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUR5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.003*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUL1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e6.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUL2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.041*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUL3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e10.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUL4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.007*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUL5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.026*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLL1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.025*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLL2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e7.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLL3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e6.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e9.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLL4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e7.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLL5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e8.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLR1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.024*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLR2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e8.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLR3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e6.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLR4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e6.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLR5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.661\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Significant at \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eP-value for precision, Wilcoxon test.\u003c/p\u003e\n\u003cp\u003eQ1= 25\u003csup\u003eth\u0026nbsp;\u003c/sup\u003epercentile, Q3= 75\u003csup\u003eth\u0026nbsp;\u003c/sup\u003epercentile, Min= Minimum, Max= Maximum. \u0026nbsp;\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":"Imaging, precision, indirect bonding, three-dimensional image, computer-aided design","lastPublishedDoi":"10.21203/rs.3.rs-6976645/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6976645/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction/Objectives:\u003c/h2\u003e\u003cp\u003eThe adoption of indirect bonding in orthodontics has grown substantially in recent years. The incorporation of computer-aided design/computer-aided manufacturing (CAD/CAM) approach can improve bracket positioning precision and potentially reduce treatment time. Additionally, cone-beam computed tomography (CBCT) enables three-dimensional evaluation of both crowns and roots, and the integration of digital models with CBCT data may enhance the precision of the indirect bonding process. This study aims to evaluate the precision of digital indirect bonding, with and without the superimposition of CBCT images onto digital models, as performed by orthodontists.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA total of 22 orthodontists participated in this study. Each orthodontist performed virtual bonding on digital models using OrthoAnalyzer\u0026trade; software (3Shape A/S, Copenhagen, Denmark) software in two stages: first, visualizing only the clinical crowns, and after a minimum interval of 15 days, visualizing both the crowns and roots with the aid of CBCT images. Bracket positioning discrepancies between the two bonding sessions were measured to assess intra-operator consistency. Positional deviations were analyzed in three dimensions: vertical, horizontal, and angular. Statistical analysis was conducted at a significance level of 5%.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003ePositional deviations were within clinically acceptable limits for both horizontal and vertical dimensions, with deviations measuring less than 0.5 mm. However, angular deviations exceeded the clinically acceptable threshold of 2\u0026deg; for certain dental elements.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe results indicate that orthodontists maintained consistent precision in the vertical and horizontal dimensions. However, the integration of CBCT data affected the precision of angular measurements.\u003c/p\u003e\u003ch2\u003eClinical significance:\u003c/h2\u003e\u003cp\u003eThese findings suggest that the incorporation of CBCT during indirect bonding may alter bracket placement in the angular dimension, which is critical for achieving optimal orthodontic outcomes.\u003c/p\u003e","manuscriptTitle":"Precision of the Indirect Bonding Method Using CAD/CAM Technology and CBCT Image Superimposition","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-22 20:06:37","doi":"10.21203/rs.3.rs-6976645/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":"c4bfaa32-26ed-4783-aa9a-97787b788be3","owner":[],"postedDate":"July 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-29T04:38:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-22 20:06:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6976645","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6976645","identity":"rs-6976645","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}