Diagnostic competence of senior dental students in detecting caries on panoramic radiographs with and without...

Accurate detection of proximal dental caries on panoramic radiographs is essential for effective treatment planning and preventive care. While senior dental students gradually develop interpretative competence during their training, artificial intelligence (AI) systems have emerged as promising adjuncts to enhance diagnostic performance. This study aimed to compare the diagnostic competence of fourth- and fifth-year dental students in detecting proximal carious lesions on panoramic radiographs, with and without AI assistance. It was hypothesized that AI-assisted evaluation would significantly improve diagnostic accuracy compared to unaided interpretation.

A total of 132 dental students (66 fourth-year and 66 fifth-year) from Biruni University participated in this cross-sectional study. Sixty anonymized panoramic radiographs, each representing a single diagnostic question, and depicting various depths of carious lesions (enamel, dentin, or pulpal) were evaluated by a specialist in oral and maxillofacial radiology and a restorative dentistry expert. Disagreements were resolved by consensus, and their evaluation served as the reference standard. Each student assessed the presence and depth of caries in two separate sessions: first unaided and then assisted by AI-based diagnostic software (DeepDent AI, DeepInsight Technologies), trained on 10,000 annotated panoramic images and designed to highlight carious regions using bounding boxes and probability scores. Statistical analyses were conducted using IBM SPSS Statistics Version 27, employing the Chi-square test, Mann-Whitney U test, and Cohen's Kappa coefficient to compare diagnostic performance and inter-rater agreement. Intra- and inter-observer reliability were high (κ = 0.82 and κ = 0.79, respectively). A p-value < 0.05 was considered statistically significant.

Fifth-year students demonstrated higher overall diagnostic performance than fourth-year students (p < 0.05). The highest rate of accurate diagnoses was observed for pulpal caries (39.4%), followed by dentin (6.1%) and enamel (5.3%) lesions. No statistically significant difference was found between the two student groups across caries depths (p > 0.05). The AI system achieved a sensitivity of 88.3%, specificity of 91.7%, and an overall accuracy of 90.1%, significantly outperforming both student groups (p < 0.001). Cohen's Kappa coefficients (κ = 0.41-0.60) indicated moderate inter-rater agreement between students and the reference standard.

Panoramic radiographs alone provide limited accuracy for detecting early or shallow proximal caries, but diagnostic performance improves with educational level. The incorporation of AI-assisted diagnostic tools into undergraduate dental curricula may enhance students' interpretative accuracy, strengthen clinical decision-making, and support early caries detection. Future studies should develop and evaluate standardized AI-integrated educational modules to optimize diagnostic proficiency in dental training.