Clinical application of deep learning for enhanced multistage caries detection in panoramic radiographs

Abstract

The detection of dental caries is typically overlooked on panoramic radiographs. This study aims to leverage deep learning to identify multistage caries on panoramic radiographs. The panoramic radiographs were confirmed with the gold standard bitewing radiographs to create a reliable ground truth. The dataset of 500 panoramic radiographs with corresponding bitewing confirmations was labelled by an experienced and calibrated radiologist for 1,792 caries from 14,997 teeth. The annotations were stored using the annotation and image markup standard to ensure consistency and reliability. The deep learning system employed a two-model approach: YOLOv5 for tooth detection and Attention U-Net for segmenting caries. The system achieved impressive results, demonstrating strong agreement with dentists for both caries counts and classifications (enamel, dentine, and pulp). However, some discrepancies exist, particularly in underestimating enamel caries. While the model occasionally overpredicts caries in healthy teeth (false positive), it prioritizes minimizing missed lesions (false negative), achieving a high recall of 0.96. Overall performance surpasses previously reported values, with an F1-score of 0.85 and an accuracy of 0.93 for caries segmentation in posterior teeth. The deep learning approach demonstrates promising potential to aid dentists in caries diagnosis, treatment planning, and dental education.