Journal of dentistry · 2025
Automated chart filing on bitewings using deep learning: enhancing clinical diagnosis in a multi-center study.
СтатьяМногоцентровое исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Cao Lingyun, van Nistelrooij Niels, Chaves Eduardo Trota, Bergé Stefaan, Cenci Maximiliano Sergio, Xi Tong, Loomans Bas, Vinayahalingam Shankeeth
Аннотация
OBJECTIVES: Bitewings are commonly used radiographs for visualizing teeth and various dental conditions. Manual labeling and diagnosis on bitewings for chart filing are time-consuming and prone to observer-dependent variations. This multi-center study proposes a deep learning (DL) approach to automate comprehensive chart filing of bitewings.
METHODS: A total of 1045 bitewings from Germany and The Netherlands were used for training and validation, and 216 from Slovakia for external testing. Annotations were performed by two dentists, one PhD researcher, and one caries expert. Hierarchical Mask DINO was developed for multi-class hierarchical end-to-end instance segmentation. Unmodified Mask DINO, SparseInst, and Mask R-CNN were used for comparison. Model performance was evaluated using F1-score, sensitivity, specificity, precision, mean average precision (mAP), and area under receiver operating characteristic curve (AUC).
RESULTS: Mask DINO models exhibited high effectiveness for tooth segmentation and labeling, achieving precision, sensitivity, and F1-scores above 0.96. Hierarchical Mask DINO outperformed the other models in dental finding classification. F1-scores for implant, crown, pontic, filling, root canal treatment (RCT), caries lesion, and calculus deposit were 0.944, 0.918, 0.952, 0.956, 0.988, 0.749, and 0.758, respectively, with specificities all above 0.95.
CONCLUSIONS: This study presented a DL-based approach for comprehensive assessment and diagnosis of bitewings, underlining its potential to enhance the efficiency and accuracy of chart filing in dental practice.
CLINICAL SIGNIFICANCE: The proposed model provided fully automated tooth segmentation and numbering, along with comprehensive segmentation of dental conditions. Dental professionals can benefit from this model for reducing manual workload and enhancing clinical diagnosis.
METHODS: A total of 1045 bitewings from Germany and The Netherlands were used for training and validation, and 216 from Slovakia for external testing. Annotations were performed by two dentists, one PhD researcher, and one caries expert. Hierarchical Mask DINO was developed for multi-class hierarchical end-to-end instance segmentation. Unmodified Mask DINO, SparseInst, and Mask R-CNN were used for comparison. Model performance was evaluated using F1-score, sensitivity, specificity, precision, mean average precision (mAP), and area under receiver operating characteristic curve (AUC).
RESULTS: Mask DINO models exhibited high effectiveness for tooth segmentation and labeling, achieving precision, sensitivity, and F1-scores above 0.96. Hierarchical Mask DINO outperformed the other models in dental finding classification. F1-scores for implant, crown, pontic, filling, root canal treatment (RCT), caries lesion, and calculus deposit were 0.944, 0.918, 0.952, 0.956, 0.988, 0.749, and 0.758, respectively, with specificities all above 0.95.
CONCLUSIONS: This study presented a DL-based approach for comprehensive assessment and diagnosis of bitewings, underlining its potential to enhance the efficiency and accuracy of chart filing in dental practice.
CLINICAL SIGNIFICANCE: The proposed model provided fully automated tooth segmentation and numbering, along with comprehensive segmentation of dental conditions. Dental professionals can benefit from this model for reducing manual workload and enhancing clinical diagnosis.