Journal of dentistry · 2026
Human and artificial intelligence performance in radiographic caries detection: ex vivo tooth section-referenced evaluation and implications for clinical decision-making.
СтатьяСравнительное исследованиеНазвание и аннотация: Перевод в очереди
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Ganss Carolina, Jung Katja, Schilling Lea, Sonderegger Sarina, Neuhaus Klaus W
Аннотация
OBJECTIVES: To evaluate the caries detection performance of a commercially available AI-system (Nostic) and human raters for radiographic caries detection using tooth sections as reference.
METHODS: Radiographs and corresponding tooth sections from extracted teeth (548 approximal and 91 cervical sites) were assessed by three dentists and AI-system (graded/probabilistic outputs). Performance versus reference was quantified using ROC/PR analyses, calibration metrics, and threshold-based measures with bootstrapped confidence intervals. Rater-AI-system agreement/disagreement subsets were analysed. For early dentine lesions (D3), four decision approaches (rater-only, AI-system-only, rater-OR-AI-system, rater-AND-AI-system) were compared using a weighted clinical-loss analysis across varying false-positive penalties.
RESULTS: For approximal enamel lesions (D1/2), AI-system was more sensitive than raters (0.60 vs 0.42) with slightly lower specificity (0.80 vs 0.84) and modest discrimination (AUC 0.70 vs 0.64). For approximal dentine lesions (D3/4), discrimination was high (AUC 0.90 AI-system vs 0.86 rater); The AI-system was more sensitive (0.84 vs 0.72) while raters were more specific (0.88 vs 0.94). For cervical dentine lesions (D3/4), both performed well (AI-system: Se/Sp 0.92/0.84; rater: 0.97/0.77; AUC 0.91 vs 0.87). In D3 decision strategies, rater-only prioritised specificity (Se/Sp 0.60/0.85), AI-system-only prioritised sensitivity (0.75/0.77), OR reduced false negatives (0.84/0.73), AND reduced false positives (0.51/0.89), with AND yielding the lowest clinical loss at higher false-positive penalties.
CONCLUSIONS: The AI-system provides complementary information that becomes clinically relevant when integrated into structured human-AI-system decision rules. Context-dependent use may support minimally invasive caries management.
CLINICAL SIGNIFICANCE: Combining human assessment with AI-system may improve preventive and operative decision-making by balancing false negatives and false positives.
METHODS: Radiographs and corresponding tooth sections from extracted teeth (548 approximal and 91 cervical sites) were assessed by three dentists and AI-system (graded/probabilistic outputs). Performance versus reference was quantified using ROC/PR analyses, calibration metrics, and threshold-based measures with bootstrapped confidence intervals. Rater-AI-system agreement/disagreement subsets were analysed. For early dentine lesions (D3), four decision approaches (rater-only, AI-system-only, rater-OR-AI-system, rater-AND-AI-system) were compared using a weighted clinical-loss analysis across varying false-positive penalties.
RESULTS: For approximal enamel lesions (D1/2), AI-system was more sensitive than raters (0.60 vs 0.42) with slightly lower specificity (0.80 vs 0.84) and modest discrimination (AUC 0.70 vs 0.64). For approximal dentine lesions (D3/4), discrimination was high (AUC 0.90 AI-system vs 0.86 rater); The AI-system was more sensitive (0.84 vs 0.72) while raters were more specific (0.88 vs 0.94). For cervical dentine lesions (D3/4), both performed well (AI-system: Se/Sp 0.92/0.84; rater: 0.97/0.77; AUC 0.91 vs 0.87). In D3 decision strategies, rater-only prioritised specificity (Se/Sp 0.60/0.85), AI-system-only prioritised sensitivity (0.75/0.77), OR reduced false negatives (0.84/0.73), AND reduced false positives (0.51/0.89), with AND yielding the lowest clinical loss at higher false-positive penalties.
CONCLUSIONS: The AI-system provides complementary information that becomes clinically relevant when integrated into structured human-AI-system decision rules. Context-dependent use may support minimally invasive caries management.
CLINICAL SIGNIFICANCE: Combining human assessment with AI-system may improve preventive and operative decision-making by balancing false negatives and false positives.