Development and evaluation of a deep learning segmentation model for assessing non-surgical endodontic treatment outcomes on periapical radiographs: A retrospective study

This study aimed to evaluate the performance of a deep learning-based segmentation model for predicting outcomes of non-surgical endodontic treatment. Preoperative and 3-year postoperative periapical radiographic images of each tooth from routine root canal treatments performed by endodontists from 2015 to 2021 were obtained retrospectively from Thammasat University hospital. Preoperative radiographic images of 1200 teeth with 3-year follow-up results (440 healed, 400 healing, and 360 disease) were collected. Mask Region-based Convolutional Neural Network (Mask R-CNN) was used to pixel-wise segment the root from other structures in the image and trained to predict class label into healed, healing and disease. Three endodontists annotated 1080 images used for model training, validation, and testing. The performance of the model was evaluated on a test set and also by comparison with the performance of clinicians (general practitioners and endodontists) with and without the help of the model on independent 120 images. The performance of the Mask R-CNN prediction model was high with the mean average precision (mAP) of 0.88 (95% CI 0.83–0.93) and area under the precision-recall curve of 0.91 (95% CI 0.88–0.94), 0.83 (95% CI 0.81–0.85), 0.91 (95% CI 0.90–0.92) on healed, healing and disease, respectively. The prediction metrics of general practitioners and endodontists significantly improved with the help of Mask R-CNN outperforming clinicians alone with mAP increasing from 0.75 (95% CI 0.72–0.78) to 0.84 (95% CI 0.81–0.87) and 0.88 (95% CI 0.85–0.91) to 0.92 (95% CI 0.89–0.95), respectively. In conclusion, deep learning-based segmentation model had the potential to predict non-surgical endodontic treatment outcomes from periapical radiographic images and were expected to aid in endodontic treatment.