Automated classification of midpalatal suture maturation using 2D convolutional neural networks on CBCT scans
IntroductionAccurate assessment of midpalatal suture (MPS) maturation is critical in orthodontics, particularly for planning treatment strategies in patients with maxillary transverse deficiency (MTD). Although cone-beam computed tomography (CBCT) provides detailed imaging suitable for MPS classific...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Dental Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdmed.2025.1583455/full |
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| author | Mahshid Nik Ravesh Nazila Ameli Manuel Lagravere Vich Hollis Lai |
| author_facet | Mahshid Nik Ravesh Nazila Ameli Manuel Lagravere Vich Hollis Lai |
| author_sort | Mahshid Nik Ravesh |
| collection | DOAJ |
| description | IntroductionAccurate assessment of midpalatal suture (MPS) maturation is critical in orthodontics, particularly for planning treatment strategies in patients with maxillary transverse deficiency (MTD). Although cone-beam computed tomography (CBCT) provides detailed imaging suitable for MPS classification, manual interpretation is often subjective and time-consuming.MethodsThis study aimed to develop and evaluate a lightweight two-dimensional convolutional neural network (2D CNN) for the automated classification of MPS maturation stages using axial CBCT slices. A retrospective dataset of CBCT images from 111 patients was annotated based on Angelieri's classification system and grouped into three clinically relevant categories: AB (Stages A and B), C, and DE (Stages D and E). A 9-layer CNN architecture was trained and evaluated using standard classification metrics and receiver operating characteristic (ROC) curve analysis.ResultsThe model achieved a test accuracy of 96.49%. Class-wise F1-scores were 0.95 for category AB, 1.00 for C, and 0.95 for DE. Area under the ROC curve (AUC) scores were 0.10 for AB, 0.62 for C, and 0.98 for DE. Lower AUC values in the early and transitional stages (AB and C) likely reflect known anatomical overlap and subjectivity in expert labeling.DiscussionThese findings indicate that the proposed 2D CNN demonstrates high accuracy and robustness in classifying MPS maturation stages from CBCT images. Its compact architecture and strong performance suggest it is suitable for real-time clinical decision-making, particularly in identifying cases that may benefit from surgical intervention. Moreover, its lightweight design makes it adaptable for use in resource-limited settings. Future work will explore volumetric models to further enhance diagnostic reliability and confidence. |
| format | Article |
| id | doaj-art-03299dae8f6b4ff6abdb0ca9b5b4e9d8 |
| institution | OA Journals |
| issn | 2673-4915 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Dental Medicine |
| spelling | doaj-art-03299dae8f6b4ff6abdb0ca9b5b4e9d82025-08-20T02:24:17ZengFrontiers Media S.A.Frontiers in Dental Medicine2673-49152025-06-01610.3389/fdmed.2025.15834551583455Automated classification of midpalatal suture maturation using 2D convolutional neural networks on CBCT scansMahshid Nik RaveshNazila AmeliManuel Lagravere VichHollis LaiIntroductionAccurate assessment of midpalatal suture (MPS) maturation is critical in orthodontics, particularly for planning treatment strategies in patients with maxillary transverse deficiency (MTD). Although cone-beam computed tomography (CBCT) provides detailed imaging suitable for MPS classification, manual interpretation is often subjective and time-consuming.MethodsThis study aimed to develop and evaluate a lightweight two-dimensional convolutional neural network (2D CNN) for the automated classification of MPS maturation stages using axial CBCT slices. A retrospective dataset of CBCT images from 111 patients was annotated based on Angelieri's classification system and grouped into three clinically relevant categories: AB (Stages A and B), C, and DE (Stages D and E). A 9-layer CNN architecture was trained and evaluated using standard classification metrics and receiver operating characteristic (ROC) curve analysis.ResultsThe model achieved a test accuracy of 96.49%. Class-wise F1-scores were 0.95 for category AB, 1.00 for C, and 0.95 for DE. Area under the ROC curve (AUC) scores were 0.10 for AB, 0.62 for C, and 0.98 for DE. Lower AUC values in the early and transitional stages (AB and C) likely reflect known anatomical overlap and subjectivity in expert labeling.DiscussionThese findings indicate that the proposed 2D CNN demonstrates high accuracy and robustness in classifying MPS maturation stages from CBCT images. Its compact architecture and strong performance suggest it is suitable for real-time clinical decision-making, particularly in identifying cases that may benefit from surgical intervention. Moreover, its lightweight design makes it adaptable for use in resource-limited settings. Future work will explore volumetric models to further enhance diagnostic reliability and confidence.https://www.frontiersin.org/articles/10.3389/fdmed.2025.1583455/fulldeep learningconvolutional neural networksmidpalatal sutureCBCTorthodonticsmaxillary transverse deficiency |
| spellingShingle | Mahshid Nik Ravesh Nazila Ameli Manuel Lagravere Vich Hollis Lai Automated classification of midpalatal suture maturation using 2D convolutional neural networks on CBCT scans Frontiers in Dental Medicine deep learning convolutional neural networks midpalatal suture CBCT orthodontics maxillary transverse deficiency |
| title | Automated classification of midpalatal suture maturation using 2D convolutional neural networks on CBCT scans |
| title_full | Automated classification of midpalatal suture maturation using 2D convolutional neural networks on CBCT scans |
| title_fullStr | Automated classification of midpalatal suture maturation using 2D convolutional neural networks on CBCT scans |
| title_full_unstemmed | Automated classification of midpalatal suture maturation using 2D convolutional neural networks on CBCT scans |
| title_short | Automated classification of midpalatal suture maturation using 2D convolutional neural networks on CBCT scans |
| title_sort | automated classification of midpalatal suture maturation using 2d convolutional neural networks on cbct scans |
| topic | deep learning convolutional neural networks midpalatal suture CBCT orthodontics maxillary transverse deficiency |
| url | https://www.frontiersin.org/articles/10.3389/fdmed.2025.1583455/full |
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