ArticleEuropean journal of orthodontics2025
Diagnostic accuracy of an artificial intelligence-based software in detecting supernumerary and congenitally missing teeth in panoramic radiographs.
Article in European journal of orthodontics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Automated tooth numbering on panoramic radiographs versus cone-beam computed tomographs: a diagnostic accuracy study of a commercial artificial intelligence system.Dento maxillo facial radiology · 2026Article
- Artificial intelligence for facial attractiveness assessment in orthodontics: model development and evaluation.European journal of orthodontics · 2026Article
- A bilingual evaluation of chatbot performance in bruxism-related information: accuracy and readability across two models.BMC oral health · 2026Article
- Evaluating AI diagnostic accuracy in approximal dental caries detection on bitewing radiographs.Clinical oral investigations · 2026Article
- Generative Artificial Intelligence and Large Language Models in Paediatric Dentistry: A Scoping Review.International dental journal · 2026Article
- Impaction Predictors and Diagnostic Performance of CBCT Versus Panoramic Radiography for Supernumerary Teeth in a Romanian Multicenter Cohort.Diagnostics (Basel, Switzerland) · 2025Article
- Assessment of AI software's diagnostic accuracy in identifying impacted teeth in panoramic radiographs.European journal of orthodontics · 2025Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
objectivesRecent 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/
methodsThree 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.
resultsFor 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.
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