2025/06/12 by Miltiadis A Makrygiannakis, Kostis Giannakopoulos, Argyro Kavadella +2 · 1 citation
paper · doi:10.1093/ejo/cjaf054
crossref issued 2025/06/12 · crossref published 2025/06/12 · crossref published-print 2025/06/12 · crossref published-online 2025/07/05 · crossref created 2025/07/05 · crossref deposited 2025/07/05 · crossref indexed 2026/08/02
Abstract Background/Objectives Recent advances in AI have enabled its application in dentistry. This study assessed the diagnostic accuracy of an AI-based model (Diagnocat™) in detecting congenitally missing and supernumerary teeth on panoramic radiographs. Materials/Methods Three groups of 50 orthopantomograms each—control, congenitally missing, and supernumerary teeth—were evaluated by two human observers and Diagnocat™. Diagnostic performance was compared using the Wilcoxon Signed Rank and McNemar’s tests. Agreement was measured using Cohen’s Kappa, and diagnostic metrics (sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV)) were computed using IBM SPSS 29.0. Results For congenitally missing teeth, Cohen’s Kappa indicated strong agreement (0.91); however, significant differences were found in the diagnostic performance (p < 0.01). The model exhibited 84.7% sensitivity, 100.0% specificity, 100.0% PPV, and 99.4% NPV. For supernumerary teeth, the agreement was moderate (Kappa = 0.60), with significant differences in the diagnostic performance (p < 0.001). Sensitivity was 43.9%, while specificity, PPV, and NPV were 100.0%, 100.0%, and 98.9%, respectively. Limitations Using convenience sampling and a retrospective design may affect generalizability and applicability. Conclusions/Implications Although the AI-based model shows promise, it is not yet able to replace human assessment as the standard for detecting missing and supernumerary teeth in panoramic radiographs.