Deep Learning-Based Detection of Separated Root Canal Instruments in Panoramic Radiographs Using a U²-Net...

Background: Separated endodontic instruments are a significant complication in root canal treatment, affecting disinfection and long-term prognosis. Their detection on panoramic radiographs is challenging, particularly in complex anatomy or for less experienced clinicians. Objectives: This study aimed to develop and evaluate a deep learning model using the U²-Net architecture for automated detection and segmentation of separated instruments in panoramic radiographs from multiple imaging systems. Methods: A total of 36,800 panoramic radiographs were retrospectively reviewed, and 191 met strict inclusion criteria. Separated instruments were manually segmented using the Computer Vision Annotation Tool. The U²-Net model was trained and evaluated using standard performance metrics: Dice coefficient, IoU, precision, recall, and F1 score. Results: The model achieved a Dice coefficient of 0.849 (95% CI: 0.840-0.857) and IoU of 0.790 (95% CI: 0.781-0.799). Precision was 0.877 (95% CI: 0.869-0.884), recall was 0.847 (95% CI: 0.839-0.855), and the F1-score was 0.861 (95% CI: 0.853-0.869). Conclusions: These results demonstrate a strong overlap between predictions and ground truth, indicating high segmentation accuracy. The U²-Net model showed robust performance across radiographs from various systems, suggesting its clinical utility in aiding detection and treatment planning. Further multicenter studies are recommended to confirm generalizability.