Fully automated deep learning framework for detection and classification of impacted mandibular third molars in panoramic radiographs
Introduction: Mandibular third molars (MTMs) are the most frequently impacted teeth, making their detection and classification essential before surgical extraction. This study aims to develop and assess the accuracy of a deep learning model for detecting and classifying impacted mandibular third mol...
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EDP Sciences
2025-01-01
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| Series: | Journal of Oral Medicine and Oral Surgery |
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| Online Access: | https://www.jomos.org/articles/mbcb/full_html/2025/01/mbcb240248/mbcb240248.html |
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| author | Veerabhadrappa Suresh Kandagal Vengusamy Sivakumar Padarha Shreyansh Iyer Kiran Yadav Seema |
| author_facet | Veerabhadrappa Suresh Kandagal Vengusamy Sivakumar Padarha Shreyansh Iyer Kiran Yadav Seema |
| author_sort | Veerabhadrappa Suresh Kandagal |
| collection | DOAJ |
| description | Introduction: Mandibular third molars (MTMs) are the most frequently impacted teeth, making their detection and classification essential before surgical extraction. This study aims to develop and assess the accuracy of a deep learning model for detecting and classifying impacted mandibular third molars (IMTMs) using panoramic radiographs (PRs). Materials and methods: The study utilized a dataset of 1100 PRs with 1200 IMTMs and 711 PRs without MTMs. An oral radiologist validated the annotations, and the data were split into training, validation, and testing sets. The Sobel Third Molar Detection Model (STMD), built on the VGG16 architecture, identified MTMs. Detected MTMs were located using the YOLOv7 model and classified per Winter’s classification via a ResNet50-based prediction model. Results: The VGG16-based detection model achieved a testing accuracy of 93.51%, with a precision of 94.64, recall of 89.47, and an F1 score of 91.97. The ResNet50-based classification model attained a testing accuracy of 92.17%, precision of 92.1, recall of 92.17, and an AUC of 98.28. These findings demonstrate the high accuracy and reliability of both models. Conclusion: VGG16 and ResNet50 integrated with YOLOv7, demonstrated high accuracy suggesting that the automatic detection and classification of IMTMs can be significantly improved using these models. |
| format | Article |
| id | doaj-art-9c38f7e10461412ca43a9fd0765217b6 |
| institution | DOAJ |
| issn | 2608-1326 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Journal of Oral Medicine and Oral Surgery |
| spelling | doaj-art-9c38f7e10461412ca43a9fd0765217b62025-08-20T03:07:24ZengEDP SciencesJournal of Oral Medicine and Oral Surgery2608-13262025-01-01311710.1051/mbcb/2025008mbcb240248Fully automated deep learning framework for detection and classification of impacted mandibular third molars in panoramic radiographsVeerabhadrappa Suresh Kandagal0https://orcid.org/0000-0003-3784-594XVengusamy Sivakumar1Padarha Shreyansh2Iyer Kiran3https://orcid.org/0000-0003-3551-870XYadav Seema4https://orcid.org/0000-0002-9773-6641Department of Oral Diagnostic Sciences, Faculty of Dentistry, SEGi UniversitySchool of Computing, Faculty of Computing and Engineering Technology, Asia Pacific University of Technology and Innovation (APU)Oxford Internet Institute, University of OxfordPreventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health SciencesDepartment of Periodontics and Implantology, Faculty of Dentistry, SEGi UniversityIntroduction: Mandibular third molars (MTMs) are the most frequently impacted teeth, making their detection and classification essential before surgical extraction. This study aims to develop and assess the accuracy of a deep learning model for detecting and classifying impacted mandibular third molars (IMTMs) using panoramic radiographs (PRs). Materials and methods: The study utilized a dataset of 1100 PRs with 1200 IMTMs and 711 PRs without MTMs. An oral radiologist validated the annotations, and the data were split into training, validation, and testing sets. The Sobel Third Molar Detection Model (STMD), built on the VGG16 architecture, identified MTMs. Detected MTMs were located using the YOLOv7 model and classified per Winter’s classification via a ResNet50-based prediction model. Results: The VGG16-based detection model achieved a testing accuracy of 93.51%, with a precision of 94.64, recall of 89.47, and an F1 score of 91.97. The ResNet50-based classification model attained a testing accuracy of 92.17%, precision of 92.1, recall of 92.17, and an AUC of 98.28. These findings demonstrate the high accuracy and reliability of both models. Conclusion: VGG16 and ResNet50 integrated with YOLOv7, demonstrated high accuracy suggesting that the automatic detection and classification of IMTMs can be significantly improved using these models.https://www.jomos.org/articles/mbcb/full_html/2025/01/mbcb240248/mbcb240248.htmlclassificationdeep learningimpacted teethmandibleradiographsthird molar |
| spellingShingle | Veerabhadrappa Suresh Kandagal Vengusamy Sivakumar Padarha Shreyansh Iyer Kiran Yadav Seema Fully automated deep learning framework for detection and classification of impacted mandibular third molars in panoramic radiographs Journal of Oral Medicine and Oral Surgery classification deep learning impacted teeth mandible radiographs third molar |
| title | Fully automated deep learning framework for detection and classification of impacted mandibular third molars in panoramic radiographs |
| title_full | Fully automated deep learning framework for detection and classification of impacted mandibular third molars in panoramic radiographs |
| title_fullStr | Fully automated deep learning framework for detection and classification of impacted mandibular third molars in panoramic radiographs |
| title_full_unstemmed | Fully automated deep learning framework for detection and classification of impacted mandibular third molars in panoramic radiographs |
| title_short | Fully automated deep learning framework for detection and classification of impacted mandibular third molars in panoramic radiographs |
| title_sort | fully automated deep learning framework for detection and classification of impacted mandibular third molars in panoramic radiographs |
| topic | classification deep learning impacted teeth mandible radiographs third molar |
| url | https://www.jomos.org/articles/mbcb/full_html/2025/01/mbcb240248/mbcb240248.html |
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