Comparison of the Artificial Intelligence Versus Traditional Radiographic Interpretation in Detecting Periapical...

To compare the performance of an AI model with that of experienced radiologists in detecting periapical periodontitis using radiographic images.

Thirty radiographic images (CBCT, panoramic, and periapical) were analyzed by an AI model and two experienced radiologists. Diagnostic accuracy, sensitivity, specificity, and confidence levels were evaluated. Statistical analyses included Chi-square tests, independent samples t-tests, and Pearson correlation analysis.

The AI model achieved 89.6% accuracy, 86.5% sensitivity, and 88.1% specificity. Radiologist 1 showed the highest performance (accuracy 98.5%, sensitivity 93.8%, specificity 96.7%), while Radiologist 2 performed slightly lower than the AI model (accuracy 81.7%, sensitivity 83.3%, specificity 80%). The AI model demonstrated the highest mean confidence level (86.5% ± 9.18). Moderate positive correlations were observed between the AI's confidence and that of Radiologist 1 (0.383) and Radiologist 2 (0.347).

The AI model demonstrated comparable performance to experienced radiologists in detecting periapical periodontitis. These findings suggest that AI could serve as a valuable tool in dental diagnostics, potentially improving efficiency and consistency. However, further research is needed to refine AI models and evaluate their performance across diverse clinical scenarios.