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Background: This study investigates the diagnostic reliability of an artificial intelligence (AI)-based software (Diagnocat) in caries, dental restorations, missing teeth, and periodontal bone loss on panoramic radiographs (PRs), comparing its performance with evaluations from three independent dental experts serving as ground truth. Methods: A total of 104 PRs were analyzed using Diagnocat, which assigned a likelihood score (0–100%) for each condition. The same images were independently evaluated by three experts. The diagnostic performance of Diagnocat was evaluated using sensitivity, specificity, and receiver operating characteristic (ROC) curve analysis, while inter-rater agreement was assessed through Cohen’s kappa (κ). Results: Diagnocat showed high overall sensitivity (99.2%), identifying nearly all conditions marked as present by human evaluators. Specificity was low (8.7%), indicating a tendency to overdiagnose. Overall accuracy was 96%, likely influenced by the coexistence of multiple conditions. Sensitivity ranged from 77% to 96%, while specificity varied: dental restorations (66%), missing teeth (68%), periodontal bone loss (71%), and caries signs (47%). The agreement was fair for dental restorations (κ = 0.39) and missing teeth (κ = 0.37), but poor for caries signs (κ = −0.15) and periodontal bone loss (κ = −0.62). Conclusions: Diagnocat shows promise as a screening tool due to its high sensitivity, but low specificity and poor agreement for certain conditions warrant cautious interpretation alongside clinical evaluation.
Mema et al. (2025) studied this question.