Caries research · 2026
AI-Based Clinical Decision Support Systems for Secondary Caries Detection and Staging on Bitewings: A Multi-Algorithm Comparison.
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Chaves Eduardo Trota, Domburg Johannes Teunis, Romero Vitor Henrique Digmayer, van Nistelrooij Niels, Vinayahalingam Shankeeth, Sezen-Hulsmans Dilek, Mendes Fausto Medeiros, Huysmans Marie-Charlotte, Cenci Maximiliano Sergio, Lima Giana Silveira
Аннотация
INTRODUCTION: Radiographic detection of caries lesions adjacent to restorations is challenging because of the limitations of two-dimensional imaging and difficulties distinguishing true lesions from restorative or anatomical radiolucencies. Artificial intelligence (AI)-based clinical decision support systems (CDSSs) have been introduced to assist radiographic interpretation; however, different AI tools may yield variable diagnostic outputs, and their comparative performance remains unclear.
OBJECTIVE: To compare the diagnostic performance of commercial and experimental AI algorithms for detecting and staging secondary caries lesions on bitewings.
METHODS: This cross-sectional diagnostic accuracy study included 200 anonymized bitewings comprising 885 restored tooth surfaces. A consensus-based reference standard classified each restored surface as non-carious, secondary caries, or indeterminate. Secondary caries lesions were further staged as enamel-stage or dentin-stage. Five commercial AI systems (Second Opinion®, CranioCatch, Diagnocat, DIO Inteligência, and Align™ X-ray Insights) and three experimental systems based on Mask R-CNN and Mask DINO architectures were tested. Diagnostic performance was assessed using sensitivity, specificity, accuracy, positive predictive value, and negative predictive value (95% confidence intervals). Comparisons were performed using generalized estimating equations adjusted for clustered data.
RESULTS: Specificity was high across all systems (0.957-0.986), whereas sensitivity was moderate (0.327-0.487), reflecting missed detections of secondary caries lesions, particularly dentin-stage lesions. Accuracy ranged from 0.882 to 0.917, with no significant differences among the evaluated systems (p ≥ 0.05). Similar patterns were observed in the additional analysis restricted to dentin-stage lesions. Misclassifications between non-carious and secondary caries surfaces were frequently associated with radiographic overlap, restoration-related radiolucencies, and cervical artifacts.
CONCLUSIONS: AI algorithms, regardless of architecture or commercial status, showed similar diagnostic capabilities and a conservative diagnostic profile, favoring specificity over sensitivity. Improvements in dataset diversity, reference-standard quality, and model explainability may further enhance the reliability of AI-assisted secondary caries detection.
OBJECTIVE: To compare the diagnostic performance of commercial and experimental AI algorithms for detecting and staging secondary caries lesions on bitewings.
METHODS: This cross-sectional diagnostic accuracy study included 200 anonymized bitewings comprising 885 restored tooth surfaces. A consensus-based reference standard classified each restored surface as non-carious, secondary caries, or indeterminate. Secondary caries lesions were further staged as enamel-stage or dentin-stage. Five commercial AI systems (Second Opinion®, CranioCatch, Diagnocat, DIO Inteligência, and Align™ X-ray Insights) and three experimental systems based on Mask R-CNN and Mask DINO architectures were tested. Diagnostic performance was assessed using sensitivity, specificity, accuracy, positive predictive value, and negative predictive value (95% confidence intervals). Comparisons were performed using generalized estimating equations adjusted for clustered data.
RESULTS: Specificity was high across all systems (0.957-0.986), whereas sensitivity was moderate (0.327-0.487), reflecting missed detections of secondary caries lesions, particularly dentin-stage lesions. Accuracy ranged from 0.882 to 0.917, with no significant differences among the evaluated systems (p ≥ 0.05). Similar patterns were observed in the additional analysis restricted to dentin-stage lesions. Misclassifications between non-carious and secondary caries surfaces were frequently associated with radiographic overlap, restoration-related radiolucencies, and cervical artifacts.
CONCLUSIONS: AI algorithms, regardless of architecture or commercial status, showed similar diagnostic capabilities and a conservative diagnostic profile, favoring specificity over sensitivity. Improvements in dataset diversity, reference-standard quality, and model explainability may further enhance the reliability of AI-assisted secondary caries detection.