Advanced AI-driven detection of interproximal caries in bitewing radiographs using YOLOv8
Abstract Dental caries is a very common chronic disease that may lead to pain, infection, and tooth loss if its diagnosis at an early stage remains undetected. Traditional methods of tactile-visual examination and bitewing radiography, are subject to intrinsic variability due to factors such as exam...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-84737-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823862499568517120 |
|---|---|
| author | Mahsa Bayati Berhrouz Alizadeh Savareh Hojjat Ahmadinejad Farzaneh Mosavat |
| author_facet | Mahsa Bayati Berhrouz Alizadeh Savareh Hojjat Ahmadinejad Farzaneh Mosavat |
| author_sort | Mahsa Bayati |
| collection | DOAJ |
| description | Abstract Dental caries is a very common chronic disease that may lead to pain, infection, and tooth loss if its diagnosis at an early stage remains undetected. Traditional methods of tactile-visual examination and bitewing radiography, are subject to intrinsic variability due to factors such as examiner experience and image quality. This variability can result in inconsistent diagnoses. Thus, the present study aimed to develop a deep learning-based AI model using the YOLOv8 algorithm for improving interproximal caries detection in bitewing radiographs. In this retrospective study on 552 radiographs, a total of 1,506 images annotated at Tehran University of Medical Science were processed. The YOLOv8 model was trained and the results were evaluated in terms of precision, recall, and the F1 score, whereby it resulted in a precision of 96.03% for enamel caries and 80.06% for dentin caries, thus showing an overall precision of 84.83%, a recall of 79.77%, and an F1 score of 82.22%. This proves its reliability in reducing false negatives and improving diagnostic accuracy. YOLOv8 enhances interproximal caries detection, offering a reliable tool for dental professionals to improve diagnostic accuracy and clinical outcomes. |
| format | Article |
| id | doaj-art-96623c1f91414b8b8b8e25de531e9086 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-96623c1f91414b8b8b8e25de531e90862025-02-09T12:29:29ZengNature PortfolioScientific Reports2045-23222025-02-0115111110.1038/s41598-024-84737-xAdvanced AI-driven detection of interproximal caries in bitewing radiographs using YOLOv8Mahsa Bayati0Berhrouz Alizadeh Savareh1Hojjat Ahmadinejad2Farzaneh Mosavat3Post Graduate Student, Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Tehran University of Medical SciencesPhD in Medical Informatic, Research and Development Manager, Department of Artificial Intelligence, Naaptech CoMSC in IT Engineering, CEO, Naaptech CoAssociate Professor, Department of Oral & Maxillofacial Radiology, Faculty of Dentistry, Tehran University of Medical SciencesAbstract Dental caries is a very common chronic disease that may lead to pain, infection, and tooth loss if its diagnosis at an early stage remains undetected. Traditional methods of tactile-visual examination and bitewing radiography, are subject to intrinsic variability due to factors such as examiner experience and image quality. This variability can result in inconsistent diagnoses. Thus, the present study aimed to develop a deep learning-based AI model using the YOLOv8 algorithm for improving interproximal caries detection in bitewing radiographs. In this retrospective study on 552 radiographs, a total of 1,506 images annotated at Tehran University of Medical Science were processed. The YOLOv8 model was trained and the results were evaluated in terms of precision, recall, and the F1 score, whereby it resulted in a precision of 96.03% for enamel caries and 80.06% for dentin caries, thus showing an overall precision of 84.83%, a recall of 79.77%, and an F1 score of 82.22%. This proves its reliability in reducing false negatives and improving diagnostic accuracy. YOLOv8 enhances interproximal caries detection, offering a reliable tool for dental professionals to improve diagnostic accuracy and clinical outcomes.https://doi.org/10.1038/s41598-024-84737-xArtificial intelligenceConvolutional neural networkDeep learningMachine learningDigital bitewing radiographyDental caries |
| spellingShingle | Mahsa Bayati Berhrouz Alizadeh Savareh Hojjat Ahmadinejad Farzaneh Mosavat Advanced AI-driven detection of interproximal caries in bitewing radiographs using YOLOv8 Scientific Reports Artificial intelligence Convolutional neural network Deep learning Machine learning Digital bitewing radiography Dental caries |
| title | Advanced AI-driven detection of interproximal caries in bitewing radiographs using YOLOv8 |
| title_full | Advanced AI-driven detection of interproximal caries in bitewing radiographs using YOLOv8 |
| title_fullStr | Advanced AI-driven detection of interproximal caries in bitewing radiographs using YOLOv8 |
| title_full_unstemmed | Advanced AI-driven detection of interproximal caries in bitewing radiographs using YOLOv8 |
| title_short | Advanced AI-driven detection of interproximal caries in bitewing radiographs using YOLOv8 |
| title_sort | advanced ai driven detection of interproximal caries in bitewing radiographs using yolov8 |
| topic | Artificial intelligence Convolutional neural network Deep learning Machine learning Digital bitewing radiography Dental caries |
| url | https://doi.org/10.1038/s41598-024-84737-x |
| work_keys_str_mv | AT mahsabayati advancedaidrivendetectionofinterproximalcariesinbitewingradiographsusingyolov8 AT berhrouzalizadehsavareh advancedaidrivendetectionofinterproximalcariesinbitewingradiographsusingyolov8 AT hojjatahmadinejad advancedaidrivendetectionofinterproximalcariesinbitewingradiographsusingyolov8 AT farzanehmosavat advancedaidrivendetectionofinterproximalcariesinbitewingradiographsusingyolov8 |