Size differences between the maxillary halves in CBCT datasets of subjects with unilateral palatal canine impactions

Size differences between the maxillary halves in CBCT datasets of subjects with unilateral palatal canine impactions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Size differences between the maxillary halves in CBCT datasets of subjects with unilateral palatal canine impactions Ahmed Baqer, Kabir Syed Gyasudeen, Rana Eljabour, Jahanzeb Chaudhry, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4124151/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To investigate asymmetry in the maxillary volume of subjects with unilateral palatal canine impactions using a novel artificial intelligence (AI)-assisted Cone-beam computed tomography (CBCT) segmentation method. Methods Craniofacial CBCT datasets of eleven subjects with unilateral palatal canine impactions were processed with a combination of AI-assisted automatic and investigator-guided segmentation techniques. Post-segmentation, three investigators independently measured the voxel-based volumes of specific maxillary structures, including the impaction and non-impaction maxillary sides, and the maxillary canines. Results High inter- and intra-investigator reliability in the volumetric measurements was seen. No significant right-left differences in the volumetric measurements of the skeletal maxillary halves ( p = 0.3) or maxillary canines ( p = 0.87) was observed in subjects with unilateral palatal canine impactions. Conclusions Within study limitations, right-left maxillary volumetric symmetry is observed in subjects with unilateral palatal canine impactions. The study establishes a reliable method for future AI-assisted investigations to understand the aetiology of canine impactions using CBCT datasets. Health sciences/Diseases/Dental diseases Health sciences/Diseases/Oral diseases Figures Figure 1 Figure 2 Figure 3 Introduction The maxilla is formed by the elevation and subsequent fusion of the right and left palatal shelves. Considerable debate exists regarding the aetiology of maxillary canine impactions, and the popular propositions include genetic theory 2 , guidance theory 3 and space inadequacy 4 .Studies have reported a general association between maxillary tooth agenesis and decreased maxillary skeletal dimensions 5 . Even in subjects with impacted canines, transverse skeletal and dental arch-width discrepancies in the maxilla have been reported 6 , 7 .Although the two halves of the maxilla are not perfectly symmetrical, it is reasonable to speculate that when canines fail to erupt, there may be an associated bone deficiency on the affected side of the maxilla. Cone-beam computed tomography (CBCT) is useful for diagnosis and treatment planning of impacted teeth 8 .Until recently, image segmentation (the process of extracting a desired region of interest) of CBCT datasets was either manual or semi-automatic 9 .However, both segmentation techniques are time-consuming, prone to operator variability, and hampered by metal artifacts 10 .Presently, the use of artificial intelligence (AI) based approaches for the fully automatic segmentation of CBCT datasets has largely overcome many of these limitations. AI-based approaches for the fully automated segmentation of anatomic structures in craniofacial CBCT datasets, including the maxilla, have been described 11 .Multiple AI models have been integrated to enable quick, automatic, and accurate segmentation of the maxillary complex, sinus, and teeth 12 .With AI-based auto-segmentation and volumetric measurements, new possibilities for investigation have emerged. For instance, researchers have recently quantified asymmetry of the maxilla in subjects with unilateral cleft lip and palate (CLP) 13 .AI-based segmentation and volumetric measurements can also help quantify asymmetry and shed light on right-left differences in subjects with unilateral canine impactions. Understanding the size differences between the two maxillary halves can provide insights into the aetiology of unilateral canine impactions. Therefore, this preliminary study aimed to 1) establish a methodology for software-assisted virtual separation of the maxillary halves and 2) compare right-left volumetric differences in the CBCT datasets of subjects with unilateral palatal canine impactions. Methods This study was approved by the institutional (Mohammed Bin Rashid University IRB-2023-45) and regional health authority (Dubai Scientific Research Ethics Committee-GL22-2023) review boards and was performed in accordance with institutional guidelines and regulations. Dataset Craniofacial CBCT datasets (n = 11) of subjects (4 male,7 female; 18.7 ± 5.9 years) with unilateral (6 left-sided, 5 right-sided) intraosseous palatally impacted canines acquired with the i-CAT 17–19 (Imaging Sciences International, Hatfield, PA, USA) (n = 10) and Veraviewepocs 3D R100 (J. Morita Corporation, Osaka, Japan) (n = 1) scanners. Scan settings for the i-CAT and the Veraviewepocs 3D units were 120 kVp, 5 mA & 8.9 seconds and 90 kVp, 2 mA, & 9.3 seconds respectively. Informed consent was obtained from all subjects and/or their legal guardians prior to scanning. The scans with suboptimal quality, partial inclusion of the region of interest, bilateral canine impactions, buccally impacted canines, history of maxillofacial trauma, post-orthognathic surgery, presence of mini-plates, presence of dental implants, craniofacial syndromes, and CLP were excluded. The scans were anonymized and new identification numbers were assigned prior to uploading them on a cloud-based segmentation and image analysis software (Relu BV, Leuven, Belgium). Automatic Segmentation Once uploaded (Fig. 1 A), sequential steps were executed to facilitate the segmentation of specific regions of interest and volumetric measurements. Initially, the automatic segmentation function of the software was used to segment the edentulous maxillary complex (Mx), impacted canine (IMxC), and non-impacted canine (NIMxC)(Fig. 1 B).Then, simultaneous visualization of the volume-rendered (Fig. 1 C) and multiplanar reformatted (MPR) views (Figs. 1 D,E,F) were enabled in the editor interface of the software. Investigator-guided segmentation : With automatic segmentation of the edentulous maxillary complex the anterior, posterior, and inferior boundaries were pre-set at the anterior nasal spine (ANS), posterior nasal spine (PNS) and alveolar crest region, respectively. Further segmentation of the edentulous maxillary complex was investigator-guided, wherein boundary conditions were established by identifying specific anatomic landmarks simultaneously in the MPR views by manually toggling between slices. The re-sliceable axis feature of the software was initially utilised to reorient the scans in the MPR views (Figs. 2A 1 , 2A 2 , & 2A 3 ) by aligning the ANS, nasopalatine canal (NC), and PNS. Then, in the midline region of the edentulous maxillary complex, the most superior surface, distal to the nasopalatine canal as observed in the sagittal (Fig. 2B 1 ) and coronal (Fig. 2B 2 ) views was chosen as the boundary to guide superior segmentation (Fig. 2B 3 ). The most distal point of the lateral wall of the maxillary sinus as observed in the axial (Fig. 2C 1 ) and coronal (Fig. 2C 2 ) views was chosen as the lateral limit to guide lateral segmentation (Fig. 2C 3 ). Once scan reorientations and landmarks were finalized, smart features of the software interface were activated to segment and remove extraneous regions from the maxillary complex (Fig. 3 A) while preserving the areas of interest (Fig. 3 B). For segmentation (Fig. 3C 1 - C 3 ), the dimensions of the cylindrical eraser tool were set to their maximum value (size:200, depth:100).Finally, ANS, PNS, and NC were again aligned in the MRP views to guide segmentation of the two halves of the maxilla, namely the maxillary half with no impaction (NIMx) and the maxillary half with the impacted canine (IMx) (Fig. 3 D). Volumetric measurements : The volume calculation tool was utilized to measure the voxel-based volumes of the segmented:1)maxillary complex (Mx), 2)maxillary half with the impacted canine (IMx), 3)maxillary half with no impaction (NIMx), 4)canine on the impacted side (IMxC), and 5)non-impacted canine (NIMxC).All measurements were performed independently by three investigators (AB-orthodontic resident; KG-recently graduated orthodontist; and SP-experienced orthodontist),repeated after a two-week memory washout period, and recorded on a spreadsheet. Statistical Analysis Data from the spreadsheet were imported, and statistical analyses performed using SPSS for Mac (version 29.0; SPSS Inc., Chicago, IL, USA). The intraclass correlation coefficient (ICC) was calculated to determine the magnitude of the inter- and intra-investigator measurement errors. The median of all measurements was used for further statistical analyses. Paired t-tests were used to compare the mean volume of the structures on the impacted (IMxC and IMx) and non-impacted halves (NIMxC and NIMx) of the maxilla. Results The inter- and intra-investigator reliability values for the volumetric measurements of the automatically segmented structures (Mx, IMxC, and NIMxC) were extremely high (ICC = 1; p ≤ 0.001 ), indicating perfect agreement. Volume of the nasomaxillary complex (Mx) ranged from 22510.39 mm 3 to 59394.82 mm 3 with a mean(± SD) of 45902.60 ± 10670.67 mm 3 . Volume of the canine on the impacted side (IMxC) ranged from 452.61mm 3 to 881.28 mm 3 with a mean(± SD) of 571.67 ± 121.54 mm 3 . Volume of the canine on the non-impacted side (NIMxC) ranged from 400.38 mm 3 to 809.15 mm 3 with a mean(± SD) of 558.78 ± 108.64 mm 3 . There was no statistically significant difference (paired Student’s t-test; p = 0.318 > 0.05 ) in the volumetric measurements of the impacted and non-impacted side canines. Similarly, the inter- and intra-investigator reliability values for the volumetric measurements of the structures segmented with guidance (IMx and NIMx) were very high (ICC > 0.900; 95% CI = 0.96–0.99; p ≤ 0.001 ), indicating excellent agreement. Volume of the side of the maxilla with the impaction (IMx) ranged from 7998.11mm 3 to 16326.63 mm 3 with a mean(± SD) of 11520.30 ± 2328.80 mm 3 .Volume of the side of the maxilla with no impaction (NIMx) ranged from 8563.62 mm 3 to 14873.28 mm 3 with a mean(± SD) of 11467.13 ± 2304.03 mm 3 . There was no statistically significant difference (paired Student’s t-test; p = 0.875 > 0.05 ) between the volumetric measurements of the maxillary halves with and without impacted canines. Discussion The need for more research using 3D tools for a better understanding of impacted maxillary canines has recently been emphasized 14 .AI-assisted tools now permit the manipulation and accurate measurement of CBCT datasets, thereby providing new opportunities for research. In this preliminary study, a combination of AI-assisted fully automatic and investigator-guided segmentation based on anatomic landmarks was utilized to delineate a specific portion of the maxillary complex for volumetric measurements. Standardised reorientation of the scans by systematic reading of the MPR views guided the segmentation process to obtain the region of interest relevant to the study. The sagittal image was used to view relevant structures in an anteroposterior orientation, such as ANS, PNS and NC. Axial and coronal views were used for mediolateral and vertical orientations, respectively. With due consideration that maxillary canines form high in the maxilla and traverse a considerable distance before reaching their destination in the arch 15 , the superior boundary for guiding segmentation was set in the nasal floor region. Similarly, the lateral limit for guiding maxillary segmentation was set at the lateral wall of the maxillary sinus. Throughout the measurement period, investigators were guided by an expert radiologist (JC) and had access to a video tutorial 16 . At each step of the guided segmentation process, AI-assisted reconstruction of the 3D volume occurred in real-time, thereby saving time. Recently, a 3D U-Net AI architecture was found to be accurate and efficient for the automatic segmentation of maxilla and cleft defect in CBCT datasets of CLP subjects. Human input was used in the study for refining post-segmentation and for removing teeth crowns 13 . However, in this study, teeth were auto segmented from the maxillary complex thereby pre-setting the inferior boundary of the maxilla and facilitating quick volumetric measurements of non-impacted and impacted canines. For volume calculations of the bony maxilla and cleft defect, previous studies were facilitated by voxel counting using proprietary software (ITK-SNAP) 13 . Similarly, voxel-based volumetric measurements were used in this study. Inter- and intra-investigator volumetric measurements of the automatically segmented structures, namely, the maxilla (Mx) and both canines (IMxC and NIMxC), showed perfect agreement, consistent with recent reports that used the same AI model for quick and precise segmentation of the maxilla 12 and impacted maxillary canines 17 .Excellent agreement was also observed among the three investigators at both time points for volumetric measurements of structures segmented under guidance(IMx and NIMx).Overall, the study results highlight the reliability of the measurement method used. However, it is important to note that the sample size was limited (n = 11) in this preliminary study and further research with a larger and more diverse sample size is warranted to generalize these findings. Future studies including subjects with buccal canine impactions and correlation analyses for age, sex, and dental maturity are needed. Several CBCT imaging protocols are available based on the size of the field of view, exposure time, and voxel size. The selection of CBCT scan parameters is based on balancing the image quality for the intended purpose with the effective radiation dose. Therefore, extending this study to include different CBCT scan settings and scanning machines will also improve the generalizability of the study findings. Nonetheless, the high consistency in volumetric measurements despite the differing experience levels of the three investigators has important implications. Recently, 3D craniofacial skeletal surface models derived from CBCT volumes were reported to be highly accurate 18 . Therefore, the method outlined in this study can be used to understand 3D morphological differences in the symmetry of craniofacial structures during normal and abnormal growth. Additionally, with appropriate modifications to the method outlined, treatment simulation and construction of personalized orthodontic appliances for the management of impacted canines is also possible. Not taking into consideration variations in the size of the soft tissue dental follicles surrounding the impacted canines 19 is another study limitation. Although, the shape of the mid-palatal suture is tortuous with age-related variations in morphology 20 , the only pre-set shape (cylinder) currently available in the software limited the investigator-guided segmentation to a straight cut. Lastly, despite setting the superior segmentation boundary in the region of the nasal floor, impacted maxillary canines that are extremely high impose restrictions on segmentation. Setting a higher superior limit for guiding the segmentation, such as the roof of the maxillary sinus, could avoid this, but would further reduce the number of measurable datasets. It should be noted that the incidence of impacted canines is relatively low and that they are commonly identified in growing children, where CBCT scans are typically restricted to a small field of view. Conclusion A novel methodology was validated for measuring the bone volume of maxillary structures in CBCT scans of subjects with unilateral palatal canine impactions. Within study limitations, no right-left differences are observed in the sizes of the maxillary halves or maxillary canines of subjects with unilateral palatal canine impactions. Declarations Author Contribution Study idea and conceptualization: Sabarinath Prasad , Jahanzeb ChaudhryObtaining funding: Ahmed Baqer, Rana Eljabour, Sabarinath Prasad, Jahanzeb Chaudhry Review and synthesis of literature: Ahmed Baqer, Kabir Syed Gyasudeen, Rana Eljabour, Abdulrahman Tawfik , Sabarinath PrasadAcquisition of data: Ahmed Baqer, Kabir Syed Gyasudeen, Sabarinath PrasadAnalysis, and interpretation of data: Sabarinath Prasad , Kabir Syed Gyasudeen, Jahanzeb ChaudhryDrafting of the manuscript: Sabarinath Prasad , Jahanzeb Chaudhry, Kabir Syed Gyasudeen,Final approval of manuscript: Sabarinath Prasad , Ahmed Baqer, Jahanzeb Chaudhry, , Kabir Syed Gyasudeen, Rana Eljabour, Abdulrahman Tawfik Acknowledgments The authors would like to thank Dr. Mohammed Jamal and Prof. Ahmed Ghoneima for facilitating the project. Data Availability All data generated or analysed during this study are included in this published article. The database used in the study is not publicly available. 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Am J Orthod Dentofacial Orthop. 2013;143(4):527–534. De Grauwe A, Ayaz I, Shujaat S, et al. CBCT in orthodontics: a systematic review on justification of CBCT in a paediatric population prior to orthodontic treatment. Eur J Orthod. 2018;41(4):381–389. Vallaeys K, Kacem A, Legoux H, Le Tenier M, Hamitouche C, Arbab-Chirani R. 3D dento-maxillary osteolytic lesion and active contour segmentation pilot study in CBCT: semi-automatic vs manual methods. Dentomaxillofac Radiol. 2015;44(8):20150079. Jacobs R, Salmon B, Codari M, Hassan B, Bornstein MM. Cone beam computed tomography in implant dentistry: recommendations for clinical use. BMC Oral Health. 2018;18(1):88. Preda F, Morgan N, Van Gerven A, et al. Deep convolutional neural network-based automated segmentation of the maxillofacial complex from cone-beam computed tomography:A validation study. J Dent. 2022;124:104238. Nogueira-Reis F, Morgan N, Nomidis S, et al. 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Deep learning driven segmentation of maxillary impacted canine on cone beam computed tomography images. Sci Rep. 2024;14(1):369. Ghamri M, Dritsas K, Probst J, et al. Accuracy of facial skeletal surfaces segmented from CT and CBCT radiographs. Sci Rep. 2023;13(1):21002. Ericson S, Bjerklin K. The Dental Follicle in Normally and Ectopically Erupting Maxillary Canines: A Computed Tomography Study. Angle Orthod. 2001;71(5):333–342. Angelieri F, Cevidanes LH, Franchi L, Gonçalves JR, Benavides E, McNamara JA, Jr. Midpalatal suture maturation: classification method for individual assessment before rapid maxillary expansion. Am J Orthod Dentofacial Orthop. 2013;144(5):759–69. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4124151","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":286761379,"identity":"cb3a323e-f81d-4aec-bd81-07556eb1b204","order_by":0,"name":"Ahmed Baqer","email":"","orcid":"","institution":"Mohammed Bin Rashid University of Medicine and Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"","lastName":"Baqer","suffix":""},{"id":286761381,"identity":"093e014a-589f-4626-8b37-a07244c2a653","order_by":1,"name":"Kabir Syed Gyasudeen","email":"","orcid":"","institution":"Mohammed Bin Rashid University of Medicine and Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Kabir","middleName":"Syed","lastName":"Gyasudeen","suffix":""},{"id":286761382,"identity":"ef6591f3-fff7-420f-9f7e-bffe3ecc96cd","order_by":2,"name":"Rana Eljabour","email":"","orcid":"","institution":"Mohammed Bin Rashid University of Medicine and Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Rana","middleName":"","lastName":"Eljabour","suffix":""},{"id":286761383,"identity":"9ac838ca-6bbd-4eb1-99df-82457ff6b7b4","order_by":3,"name":"Jahanzeb Chaudhry","email":"","orcid":"","institution":"Mohammed Bin Rashid University of Medicine and Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jahanzeb","middleName":"","lastName":"Chaudhry","suffix":""},{"id":286761384,"identity":"01b779f3-8fe5-4bbc-ae2d-35f1d88c3ce0","order_by":4,"name":"Sabarinath Prasad","email":"data:image/png;base64,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","orcid":"","institution":"Mohammed Bin Rashid University of Medicine and Health Sciences","correspondingAuthor":true,"prefix":"","firstName":"Sabarinath","middleName":"","lastName":"Prasad","suffix":""},{"id":286761385,"identity":"d21ce07e-dff8-4956-a343-1d04245028a2","order_by":5,"name":"Abdulrahman Tawfik","email":"","orcid":"","institution":"Mohammed Bin Rashid University of Medicine and Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Abdulrahman","middleName":"","lastName":"Tawfik","suffix":""}],"badges":[],"createdAt":"2024-03-18 14:47:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4124151/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4124151/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54054938,"identity":"c894852d-fc37-4fe2-9b55-514f4dcafb1e","added_by":"auto","created_at":"2024-04-04 00:53:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2590389,"visible":true,"origin":"","legend":"\u003cp\u003e(A) - 3D view of the craniofacial region including the maxillary complex (Mx), maxillary sinuses, inferior alveolar nerve, and mandible coded in a different colour. Note the arrows pointing to the impacted (IMxC) canine and non-impacted canine (NIMxC). (B) – Shows the maxillary complex (Mx) in blue and the impacted (IMxC) and non-impacted canines (NIMxC) in florescent green after auto-segmentation. Note the arrow pointing to the anterior nasal spine (ANS) which is the pre-set anterior limit after auto-segmentation. (C) – Another view of the auto-segmented maxillary complex (Mx). Note the alveolar crest (AC) region which is the pre-set inferior limit after auto-segmentation. (D,E \u0026amp;F) – MRP views corresponding to the sagittal (D) , axial (E), and coronal (F) views respectively. Note the arrows pointing to the anterior nasal spine (ANS), posterior nasal spine (PNS), and nasopalatine canal (NC) in the sagittal (D) view; and the ANS and PNS in the axial (E) view.\u003c/p\u003e","description":"","filename":"Fig11.png","url":"https://assets-eu.researchsquare.com/files/rs-4124151/v1/c793faa1372f7e01259dc531.png"},{"id":54054939,"identity":"85fb8431-0570-4987-8d81-71116bb4d524","added_by":"auto","created_at":"2024-04-04 00:53:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2414626,"visible":true,"origin":"","legend":"\u003cp\u003e(A\u003csub\u003e1\u003c/sub\u003e to A\u003csub\u003e3\u003c/sub\u003e) \u0026nbsp;- In the top row the MPR views used to reorient the scan with respect to the ANS, PNS and NC in the sagittal (A\u003csub\u003e1\u003c/sub\u003e), and axial (A\u003csub\u003e3\u003c/sub\u003e) views are seen. (B\u003csub\u003e1\u003c/sub\u003e to B\u003csub\u003e3\u003c/sub\u003e) - In the middle row the MPR views used to reorient the scan and the volume rendered views for investigator-guided superior segmentation are seen. (C\u003csub\u003e1\u003c/sub\u003e to C\u003csub\u003e3\u003c/sub\u003e) - In the bottom row the MPR views used to reorient the scan and the volume rendered views for investigator-guided superior segmentation are seen. Note the intersections of the cross hairs in the MPR views that are delineating the boundaries for superior (B\u003csub\u003e1\u003c/sub\u003e and B\u003csub\u003e2\u003c/sub\u003e) and lateral (C\u003csub\u003e1\u003c/sub\u003e and C\u003csub\u003e2\u003c/sub\u003e)\u0026nbsp; segmentations. Note the arrows pointing to the red cylindrical eraser tool in the middle row and lower row, delineating the boundaries for superior (B\u003csub\u003e1\u003c/sub\u003e and B\u003csub\u003e2\u003c/sub\u003e) and lateral segmentations (C\u003csub\u003e1\u003c/sub\u003e and C\u003csub\u003e2\u003c/sub\u003e) in the MRP views, and for superior (B\u003csub\u003e3\u003c/sub\u003e) and lateral segmentations (C\u003csub\u003e3\u003c/sub\u003e) in the volume rendered views.\u003c/p\u003e","description":"","filename":"Fig21.png","url":"https://assets-eu.researchsquare.com/files/rs-4124151/v1/b6714a5324f33838b77deefd.png"},{"id":54054937,"identity":"df6fb524-57a5-4ceb-848d-2ff7e14107b3","added_by":"auto","created_at":"2024-04-04 00:53:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2134745,"visible":true,"origin":"","legend":"\u003cp\u003e(A) - Auto-segmented maxillary complex (Mx). (B) - More specific region of interest of the maxilla after investigator-guided superior and lateral segmentation. (C\u003csub\u003e1\u003c/sub\u003e) - Note the \u0026nbsp;arrows pointing to the red cylindrical eraser tool facilitating quick, real-time segmentation of the side of the maxilla with the impacted canine, prior to volumetric measurements in the volume rendered view. Note that the placement of the cylindrical eraser tool is guided by the anatomic landmarks AND, PNS, and NC in the sagittal MPR (C\u003csub\u003e2\u003c/sub\u003e) view and axial MPR (C\u003csub\u003e3\u003c/sub\u003e) view. (D) – The two halves of the maxillae after investigator-guided segmentation, with (IMx) and without (NIMx) the impacted canines are seen. Note the arrow pointing to the region of the impacted canine.\u003c/p\u003e","description":"","filename":"Fig31.png","url":"https://assets-eu.researchsquare.com/files/rs-4124151/v1/b701bfa05a38ab3443eadfb6.png"},{"id":56832691,"identity":"ef1bc4e2-f8c5-40a1-b6e1-1c83a40f30ac","added_by":"auto","created_at":"2024-05-21 05:01:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7649679,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4124151/v1/79661eb2-7d37-44a3-b747-ae8f05ec107a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Size differences between the maxillary halves in CBCT datasets of subjects with unilateral palatal canine impactions","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe maxilla is formed by the elevation and subsequent fusion of the right and left palatal shelves. Considerable debate exists regarding the aetiology of maxillary canine impactions, and the popular propositions include genetic theory\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, guidance theory\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e and space inadequacy\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.Studies have reported a general association between maxillary tooth agenesis and decreased maxillary skeletal dimensions\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Even in subjects with impacted canines, transverse skeletal and dental arch-width discrepancies in the maxilla have been reported\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.Although the two halves of the maxilla are not perfectly symmetrical, it is reasonable to speculate that when canines fail to erupt, there may be an associated bone deficiency on the affected side of the maxilla.\u003c/p\u003e \u003cp\u003eCone-beam computed tomography (CBCT) is useful for diagnosis and treatment planning of impacted teeth\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.Until recently, image segmentation (the process of extracting a desired region of interest) of CBCT datasets was either manual or semi-automatic\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.However, both segmentation techniques are time-consuming, prone to operator variability, and hampered by metal artifacts\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.Presently, the use of artificial intelligence (AI) based approaches for the fully automatic segmentation of CBCT datasets has largely overcome many of these limitations. AI-based approaches for the fully automated segmentation of anatomic structures in craniofacial CBCT datasets, including the maxilla, have been described\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.Multiple AI models have been integrated to enable quick, automatic, and accurate segmentation of the maxillary complex, sinus, and teeth\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.With AI-based auto-segmentation and volumetric measurements, new possibilities for investigation have emerged. For instance, researchers have recently quantified asymmetry of the maxilla in subjects with unilateral cleft lip and palate (CLP)\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.AI-based segmentation and volumetric measurements can also help quantify asymmetry and shed light on right-left differences in subjects with unilateral canine impactions. Understanding the size differences between the two maxillary halves can provide insights into the aetiology of unilateral canine impactions. Therefore, this preliminary study aimed to 1) establish a methodology for software-assisted virtual separation of the maxillary halves and 2) compare right-left volumetric differences in the CBCT datasets of subjects with unilateral palatal canine impactions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study was approved by the institutional (Mohammed Bin Rashid University IRB-2023-45) and regional health authority (Dubai Scientific Research Ethics Committee-GL22-2023) review boards and was performed in accordance with institutional guidelines and regulations.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDataset\u003c/strong\u003e \u003cp\u003eCraniofacial CBCT datasets (n\u0026thinsp;=\u0026thinsp;11) of subjects (4 male,7 female; 18.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9 years) with unilateral (6 left-sided, 5 right-sided) intraosseous palatally impacted canines acquired with the i-CAT 17\u0026ndash;19 (Imaging Sciences International, Hatfield, PA, USA) (n\u0026thinsp;=\u0026thinsp;10) and Veraviewepocs 3D R100 (J. Morita Corporation, Osaka, Japan) (n\u0026thinsp;=\u0026thinsp;1) scanners. Scan settings for the i-CAT and the Veraviewepocs 3D units were 120 kVp, 5 mA \u0026amp; 8.9 seconds and 90 kVp, 2 mA, \u0026amp; 9.3 seconds respectively. Informed consent was obtained from all subjects and/or their legal guardians prior to scanning.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe scans with suboptimal quality, partial inclusion of the region of interest, bilateral canine impactions, buccally impacted canines, history of maxillofacial trauma, post-orthognathic surgery, presence of mini-plates, presence of dental implants, craniofacial syndromes, and CLP were excluded. The scans were anonymized and new identification numbers were assigned prior to uploading them on a cloud-based segmentation and image analysis software (Relu BV, Leuven, Belgium).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAutomatic Segmentation\u003c/strong\u003e \u003cp\u003eOnce uploaded (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), sequential steps were executed to facilitate the segmentation of specific regions of interest and volumetric measurements. Initially, the automatic segmentation function of the software was used to segment the edentulous maxillary complex (Mx), impacted canine (IMxC), and non-impacted canine (NIMxC)(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).Then, simultaneous visualization of the volume-rendered (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) and multiplanar reformatted (MPR) views (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD,E,F) were enabled in the editor interface of the software.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eInvestigator-guided segmentation\u003c/em\u003e: With automatic segmentation of the edentulous maxillary complex the anterior, posterior, and inferior boundaries were pre-set at the anterior nasal spine (ANS), posterior nasal spine (PNS) and alveolar crest region, respectively. Further segmentation of the edentulous maxillary complex was investigator-guided, wherein boundary conditions were established by identifying specific anatomic landmarks simultaneously in the MPR views by manually toggling between slices. The re-sliceable axis feature of the software was initially utilised to reorient the scans in the MPR views (Figs.\u0026nbsp;2A\u003csub\u003e1\u003c/sub\u003e, 2A\u003csub\u003e2\u003c/sub\u003e, \u0026amp; 2A\u003csub\u003e3\u003c/sub\u003e) by aligning the ANS, nasopalatine canal (NC), and PNS. Then, in the midline region of the edentulous maxillary complex, the most superior surface, distal to the nasopalatine canal as observed in the sagittal (Fig.\u0026nbsp;2B\u003csub\u003e1\u003c/sub\u003e) and coronal (Fig.\u0026nbsp;2B\u003csub\u003e2\u003c/sub\u003e) views was chosen as the boundary to guide superior segmentation (Fig.\u0026nbsp;2B\u003csub\u003e3\u003c/sub\u003e). The most distal point of the lateral wall of the maxillary sinus as observed in the axial (Fig.\u0026nbsp;2C\u003csub\u003e1\u003c/sub\u003e) and coronal (Fig.\u0026nbsp;2C\u003csub\u003e2\u003c/sub\u003e) views was chosen as the lateral limit to guide lateral segmentation (Fig.\u0026nbsp;2C\u003csub\u003e3\u003c/sub\u003e). Once scan reorientations and landmarks were finalized, smart features of the software interface were activated to segment and remove extraneous regions from the maxillary complex (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) while preserving the areas of interest (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). For segmentation (Fig.\u0026nbsp;3C\u003csub\u003e1\u003c/sub\u003e - C\u003csub\u003e3\u003c/sub\u003e), the dimensions of the cylindrical eraser tool were set to their maximum value (size:200, depth:100).Finally, ANS, PNS, and NC were again aligned in the MRP views to guide segmentation of the two halves of the maxilla, namely the maxillary half with no impaction (NIMx) and the maxillary half with the impacted canine (IMx) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eVolumetric measurements\u003c/em\u003e: The volume calculation tool was utilized to measure the voxel-based volumes of the segmented:1)maxillary complex (Mx), 2)maxillary half with the impacted canine (IMx), 3)maxillary half with no impaction (NIMx), 4)canine on the impacted side (IMxC), and 5)non-impacted canine (NIMxC).All measurements were performed independently by three investigators (AB-orthodontic resident; KG-recently graduated orthodontist; and SP-experienced orthodontist),repeated after a two-week memory washout period, and recorded on a spreadsheet.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStatistical Analysis\u003c/strong\u003e \u003cp\u003eData from the spreadsheet were imported, and statistical analyses performed using SPSS for Mac (version 29.0; SPSS Inc., Chicago, IL, USA). The intraclass correlation coefficient (ICC) was calculated to determine the magnitude of the inter- and intra-investigator measurement errors. The median of all measurements was used for further statistical analyses. Paired t-tests were used to compare the mean volume of the structures on the impacted (IMxC and IMx) and non-impacted halves (NIMxC and NIMx) of the maxilla.\u003c/p\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe inter- and intra-investigator reliability values for the volumetric measurements of the automatically segmented structures (Mx, IMxC, and NIMxC) were extremely high (ICC\u0026thinsp;=\u0026thinsp;1; \u003cem\u003ep\u0026thinsp;\u0026le;\u0026thinsp;0.001\u003c/em\u003e), indicating perfect agreement. Volume of the nasomaxillary complex (Mx) ranged from 22510.39 mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e to 59394.82 mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e with a mean(\u0026plusmn;\u0026thinsp;SD) of 45902.60\u0026thinsp;\u0026plusmn;\u0026thinsp;10670.67 mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Volume of the canine on the impacted side (IMxC) ranged from 452.61mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e to 881.28 mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e with a mean(\u0026plusmn;\u0026thinsp;SD) of 571.67\u0026thinsp;\u0026plusmn;\u0026thinsp;121.54 mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Volume of the canine on the non-impacted side (NIMxC) ranged from 400.38 mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e to 809.15 mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e with a mean(\u0026plusmn;\u0026thinsp;SD) of 558.78\u0026thinsp;\u0026plusmn;\u0026thinsp;108.64 mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. There was no statistically significant difference (paired Student\u0026rsquo;s t-test; \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.318\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/em\u003e) in the volumetric measurements of the impacted and non-impacted side canines.\u003c/p\u003e \u003cp\u003eSimilarly, the inter- and intra-investigator reliability values for the volumetric measurements of the structures segmented with guidance (IMx and NIMx) were very high (ICC\u0026thinsp;\u0026gt;\u0026thinsp;0.900; 95% CI\u0026thinsp;=\u0026thinsp;0.96\u0026ndash;0.99; \u003cem\u003ep\u0026thinsp;\u0026le;\u0026thinsp;0.001\u003c/em\u003e), indicating excellent agreement. Volume of the side of the maxilla with the impaction (IMx) ranged from 7998.11mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e to 16326.63 mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e with a mean(\u0026plusmn;\u0026thinsp;SD) of 11520.30\u0026thinsp;\u0026plusmn;\u0026thinsp;2328.80 mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.Volume of the side of the maxilla with no impaction (NIMx) ranged from 8563.62 mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e to 14873.28 mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e with a mean(\u0026plusmn;\u0026thinsp;SD) of 11467.13\u0026thinsp;\u0026plusmn;\u0026thinsp;2304.03 mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. There was no statistically significant difference (paired Student\u0026rsquo;s t-test; \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.875\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/em\u003e) between the volumetric measurements of the maxillary halves with and without impacted canines.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe need for more research using 3D tools for a better understanding of impacted maxillary canines has recently been emphasized\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.AI-assisted tools now permit the manipulation and accurate measurement of CBCT datasets, thereby providing new opportunities for research. In this preliminary study, a combination of AI-assisted fully automatic and investigator-guided segmentation based on anatomic landmarks was utilized to delineate a specific portion of the maxillary complex for volumetric measurements.\u003c/p\u003e \u003cp\u003eStandardised reorientation of the scans by systematic reading of the MPR views guided the segmentation process to obtain the region of interest relevant to the study. The sagittal image was used to view relevant structures in an anteroposterior orientation, such as ANS, PNS and NC. Axial and coronal views were used for mediolateral and vertical orientations, respectively. With due consideration that maxillary canines form high in the maxilla and traverse a considerable distance before reaching their destination in the arch\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, the superior boundary for guiding segmentation was set in the nasal floor region. Similarly, the lateral limit for guiding maxillary segmentation was set at the lateral wall of the maxillary sinus. Throughout the measurement period, investigators were guided by an expert radiologist (JC) and had access to a video tutorial\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. At each step of the guided segmentation process, AI-assisted reconstruction of the 3D volume occurred in real-time, thereby saving time.\u003c/p\u003e \u003cp\u003eRecently, a 3D U-Net AI architecture was found to be accurate and efficient for the automatic segmentation of maxilla and cleft defect in CBCT datasets of CLP subjects. Human input was used in the study for refining post-segmentation and for removing teeth crowns\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, in this study, teeth were auto segmented from the maxillary complex thereby pre-setting the inferior boundary of the maxilla and facilitating quick volumetric measurements of non-impacted and impacted canines. For volume calculations of the bony maxilla and cleft defect, previous studies were facilitated by voxel counting using proprietary software (ITK-SNAP)\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Similarly, voxel-based volumetric measurements were used in this study. Inter- and intra-investigator volumetric measurements of the automatically segmented structures, namely, the maxilla (Mx) and both canines (IMxC and NIMxC), showed perfect agreement, consistent with recent reports that used the same AI model for quick and precise segmentation of the maxilla\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e and impacted maxillary canines\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.Excellent agreement was also observed among the three investigators at both time points for volumetric measurements of structures segmented under guidance(IMx and NIMx).Overall, the study results highlight the reliability of the measurement method used.\u003c/p\u003e \u003cp\u003eHowever, it is important to note that the sample size was limited (n\u0026thinsp;=\u0026thinsp;11) in this preliminary study and further research with a larger and more diverse sample size is warranted to generalize these findings. Future studies including subjects with buccal canine impactions and correlation analyses for age, sex, and dental maturity are needed. Several CBCT imaging protocols are available based on the size of the field of view, exposure time, and voxel size. The selection of CBCT scan parameters is based on balancing the image quality for the intended purpose with the effective radiation dose. Therefore, extending this study to include different CBCT scan settings and scanning machines will also improve the generalizability of the study findings. Nonetheless, the high consistency in volumetric measurements despite the differing experience levels of the three investigators has important implications. Recently, 3D craniofacial skeletal surface models derived from CBCT volumes were reported to be highly accurate\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Therefore, the method outlined in this study can be used to understand 3D morphological differences in the symmetry of craniofacial structures during normal and abnormal growth. Additionally, with appropriate modifications to the method outlined, treatment simulation and construction of personalized orthodontic appliances for the management of impacted canines is also possible.\u003c/p\u003e \u003cp\u003eNot taking into consideration variations in the size of the soft tissue dental follicles surrounding the impacted canines\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e is another study limitation. Although, the shape of the mid-palatal suture is tortuous with age-related variations in morphology\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, the only pre-set shape (cylinder) currently available in the software limited the investigator-guided segmentation to a straight cut. Lastly, despite setting the superior segmentation boundary in the region of the nasal floor, impacted maxillary canines that are extremely high impose restrictions on segmentation. Setting a higher superior limit for guiding the segmentation, such as the roof of the maxillary sinus, could avoid this, but would further reduce the number of measurable datasets. It should be noted that the incidence of impacted canines is relatively low and that they are commonly identified in growing children, where CBCT scans are typically restricted to a small field of view.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eA novel methodology was validated for measuring the bone volume of maxillary structures in CBCT scans of subjects with unilateral palatal canine impactions. Within study limitations, no right-left differences are observed in the sizes of the maxillary halves or maxillary canines of subjects with unilateral palatal canine impactions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eStudy idea and conceptualization: Sabarinath Prasad , Jahanzeb ChaudhryObtaining funding: Ahmed Baqer, Rana Eljabour, Sabarinath Prasad, Jahanzeb Chaudhry Review and synthesis of literature: Ahmed Baqer, Kabir Syed Gyasudeen, Rana Eljabour, Abdulrahman Tawfik , Sabarinath PrasadAcquisition of data: Ahmed Baqer, Kabir Syed Gyasudeen, Sabarinath PrasadAnalysis, and interpretation of data: Sabarinath Prasad , Kabir Syed Gyasudeen, Jahanzeb ChaudhryDrafting of the manuscript: Sabarinath Prasad , Jahanzeb Chaudhry, Kabir Syed Gyasudeen,Final approval of manuscript: Sabarinath Prasad , Ahmed Baqer, Jahanzeb Chaudhry, , Kabir Syed Gyasudeen, Rana Eljabour, Abdulrahman Tawfik\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe authors would like to thank Dr. Mohammed Jamal and Prof. Ahmed Ghoneima for facilitating the project.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated or analysed during this study are included in this published article. The database used in the study is not publicly available.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBecker A, Chaushu S. Etiology of maxillary canine impaction: A review. Am J Orthod Dentofacial Orthop. 2015;148(4):557\u0026ndash;567.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePirinen S, Arte S, Apajalahti S. Palatal displacement of canine is genetic and related to congenital absence of teeth. J Dent Res. 1996;75(10):1742\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBecker A. In defense of the guidance theory of palatal canine displacement. Angle Orthod. 1995;65(2):95\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJacoby H. The etiology of maxillary canine impactions. Am J Orthod. 1983;84(2):125\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTavajohi-Kermani H, Kapur R, Sciote JJ. Tooth agenesis and craniofacial morphology in an orthodontic population. Am J Orthod Dentofacial Orthop. 2002;122(1):39\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchindel RH, Duffy SL. Maxillary Transverse Discrepancies and Potentially Impacted Maxillary Canines in Mixed-dentition Patients. Angle Orthod. 2007;77(3):430\u0026ndash;435.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan B, Sun Z, Fields H, Wang L, Luo L. Etiologic factors for buccal and palatal maxillary canine impaction: a perspective based on cone-beam computed tomography analyses. Am J Orthod Dentofacial Orthop. 2013;143(4):527\u0026ndash;534.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Grauwe A, Ayaz I, Shujaat S, et al. CBCT in orthodontics: a systematic review on justification of CBCT in a paediatric population prior to orthodontic treatment. Eur J Orthod. 2018;41(4):381\u0026ndash;389.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVallaeys K, Kacem A, Legoux H, Le Tenier M, Hamitouche C, Arbab-Chirani R. 3D dento-maxillary osteolytic lesion and active contour segmentation pilot study in CBCT: semi-automatic vs manual methods. Dentomaxillofac Radiol. 2015;44(8):20150079.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJacobs R, Salmon B, Codari M, Hassan B, Bornstein MM. Cone beam computed tomography in implant dentistry: recommendations for clinical use. BMC Oral Health. 2018;18(1):88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePreda F, Morgan N, Van Gerven A, et al. Deep convolutional neural network-based automated segmentation of the maxillofacial complex from cone-beam computed tomography:A validation study. J Dent. 2022;124:104238.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNogueira-Reis F, Morgan N, Nomidis S, et al. Three-dimensional maxillary virtual patient creation by convolutional neural network-based segmentation on cone-beam computed tomography images. Clin Oral Investig.2023;27(3):1133\u0026ndash;1141.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Pastewait M, Wu TH, et al. 3D morphometric quantification of maxillae and defects for patients with unilateral cleft palate via deep learning-based CBCT image auto-segmentation. Orthod Craniofac Res. 2021;24 (Suppl 2):108\u0026ndash;116.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRavi I, Srinivasan B, Kailasam V. Radiographic predictors of maxillary canine impaction in mixed and early permanent dentition - A systematic review and meta-analysis. Int Orthod. 2021;19(4):548\u0026ndash;565.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoulter J, Richardson A. Normal eruption of the maxillary canine quantified in three dimensions. Eur J Orthod. 1997;19(2):171\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlmpani K, Adjei A, Liberton DK, Verma P, Hung M, Lee JS. Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans. J Vis Exp. 2023;(199):\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3791/65224\u003c/span\u003e\u003cspan address=\"10.3791/65224\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSwaity A, Elgarba BM, Morgan N, et al. Deep learning driven segmentation of maxillary impacted canine on cone beam computed tomography images. Sci Rep. 2024;14(1):369.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhamri M, Dritsas K, Probst J, et al. Accuracy of facial skeletal surfaces segmented from CT and CBCT radiographs. Sci Rep. 2023;13(1):21002.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEricson S, Bjerklin K. The Dental Follicle in Normally and Ectopically Erupting Maxillary Canines: A Computed Tomography Study. Angle Orthod. 2001;71(5):333\u0026ndash;342.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAngelieri F, Cevidanes LH, Franchi L, Gon\u0026ccedil;alves JR, Benavides E, McNamara JA, Jr. Midpalatal suture maturation: classification method for individual assessment before rapid maxillary expansion. Am J Orthod Dentofacial Orthop. 2013;144(5):759\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"","lastPublishedDoi":"10.21203/rs.3.rs-4124151/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4124151/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo investigate asymmetry in the maxillary volume of subjects with unilateral palatal canine impactions using a novel artificial intelligence (AI)-assisted Cone-beam computed tomography (CBCT) segmentation method.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eCraniofacial CBCT datasets of eleven subjects with unilateral palatal canine impactions were processed with a combination of AI-assisted automatic and investigator-guided segmentation techniques. Post-segmentation, three investigators independently measured the voxel-based volumes of specific maxillary structures, including the impaction and non-impaction maxillary sides, and the maxillary canines.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHigh inter- and intra-investigator reliability in the volumetric measurements was seen. No significant right-left differences in the volumetric measurements of the skeletal maxillary halves (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.3)\u003c/em\u003e or maxillary canines (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.87)\u003c/em\u003e was observed in subjects with unilateral palatal canine impactions.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWithin study limitations, right-left maxillary volumetric symmetry is observed in subjects with unilateral palatal canine impactions. The study establishes a reliable method for future AI-assisted investigations to understand the aetiology of canine impactions using CBCT datasets.\u003c/p\u003e","manuscriptTitle":"Size differences between the maxillary halves in CBCT datasets of subjects with unilateral palatal canine impactions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-04 00:53:54","doi":"10.21203/rs.3.rs-4124151/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":"405d24f6-ce38-4346-ba1c-0ed819a6e7ee","owner":[],"postedDate":"April 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30185369,"name":"Health sciences/Diseases/Dental diseases"},{"id":30185370,"name":"Health sciences/Diseases/Oral diseases"}],"tags":[],"updatedAt":"2024-05-21T04:53:42+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-04 00:53:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4124151","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4124151","identity":"rs-4124151","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}