Evaluation of a YOLOv5x-based deep learning model for interproximal caries segmentation on bitewing radiographs across primary and permanent teeth.

BACKGROUND

This study aimed to develop and evaluate a YOLOv5x-based instance segmentation model for automatic interproximal caries segmentation on bitewing radiographs across primary and permanent teeth and to summarize performance using clinically interpretable metrics.

METHODS

The current research had a retrospective study design. 1000 primary teeth and 1000 permanent teeth bitewing radiographs were analysed for interproximal caries, which were marked using polygonal segmentation in the Craniocatch labelling module, resulting in a total of 5704 caries lesions being annotated. The images were divided into 80% training, 10% validation, and 10% testing, and the Yolov5x model was trained in the PyTorch environment. Performance was reported using metrics such as confusion matrix, precision, recall, F1 score and ROC-AUC.

RESULTS

Precision value in the test dataset of primary tooth radiographs was determined as 0.80, recall as 0.78, F1 score as 0.79 and ROC-AUC as 0.87. In the permanent tooth group, precision was 0.78, recall 0.75, F1 score 0.76 and ROC-AUC value 0.76.

CONCLUSIONS

Within the limitations of the study, confusion matrices of primary and permanent tooth radiographs showed that the false positive and false negative rates were at acceptable levels. Yolov5x model is considered to have potential for use in children as a diagnostic tool, treatment planning, and clinical decision support system. However, the single-centre study design and the training process based solely on radiographic images may limit generalisability. Therefore, future studies with multi-centre, age-balanced, and multimodal data, including external validation, are recommended.