Deep Learning-Based Algorithm for Staging Secondary Caries in Bitewings

• Published in: Caries research Vol. 59; no. 3; pp. 163 - 173
• Main Authors: van Nistelrooij, Niels, Chaves, Eduardo Trota, Cenci, Maximiliano Sergio, Cao, Lingyun, Loomans, Bas A.C., Xi, Tong, El Ghoul, Khalid, Romero, Vitor Henrique Digmayer, Lima, Giana Silveira, Flügge, Tabea, van Ginneken, Bram, Huysmans, Marie-Charlotte, Vinayahalingam, Shankeeth, Mendes, Fausto Medeiros
• Format: Journal Article
• Language: English
• Published: Basel, Switzerland 01.07.2025
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Algorithms; Deep Learning; Dental Caries - diagnosis; Dental Caries - pathology; Female; Humans; Male; Middle Aged; Neural Networks, Computer; Research Article; Sensitivity and Specificity; Severity of Illness Index; Young Adult; Radiography; Caries detection; Diagnostic imaging; Artificial intelligence; Convolutional neural network
• ISSN: 0008-6568; 1421-976X; 1421-976X
• DOI: 10.1159/000542289

AbstractIntroduction: Despite the notable progress in developing artificial intelligence-based tools for caries detection in bitewings, limited research has addressed the detection and staging of secondary caries. Therefore, we aimed to develop a convolutional neural network (CNN)-based algorithm for these purposes using a novel approach for determining lesion severity. Methods: We used a dataset from a Dutch dental practice-based research network containing 2,612 restored teeth in 413 bitewings from 383 patients aged 15–88 years and trained the Mask R-CNN architecture with a Swin Transformer backbone. Two-stage training fine-tuned caries detection accuracy and severity assessment. Annotations of caries around restorations were made by two evaluators and checked by two other experts. Aggregated accuracy metrics (mean ± standard deviation – SD) in detecting teeth with secondary caries were calculated considering two thresholds: detecting all lesions and dentine lesions. The correlation between the lesion severity scores obtained with the algorithm and the annotators’ consensus was determined using the Pearson correlation coefficient and Bland-Altman plots. Results: Our refined algorithm showed high specificity in detecting all lesions (0.966 ± 0.025) and dentine lesions (0.964 ± 0.019). Sensitivity values were lower: 0.737 ± 0.079 for all lesions and 0.808 ± 0.083 for dentine lesions. The areas under ROC curves (SD) were 0.940 (0.025) for all lesions and 0.946 (0.023) for dentine lesions. The correlation coefficient for severity scores was 0.802. Conclusion: We developed an improved algorithm to support clinicians in detecting and staging secondary caries in bitewing, incorporating an innovative approach for annotation, considering the lesion severity as a continuous outcome.