Detection of Fractured Endodontic Instruments in Periapical Radiographs: A Comparative Study of YOLOv8 and Mask R-CNN

<b>Background/Objectives:</b> Accurate localization of fractured endodontic instruments (FEIs) in periapical radiographs (PAs) remains a significant challenge. This study aimed to evaluate the performance of YOLOv8 and Mask R-CNN in detecting FEIs and root canal treatments (RCTs) and compare their diagnostic capabilities with those of experienced endodontists. <b>Methods:</b> A data set of 1050 annotated PAs was used. Mask R-CNN and YOLOv8 models were trained and evaluated for FEI and RCT detection. Metrics including accuracy, intersection over union (IoU), mean average precision at 0.5 IoU (mAP50), and inference time were analyzed. Observer agreement was assessed using inter-class correlation (ICC), and comparisons were made between AI predictions and human annotations. <b>Results</b>: YOLOv8 achieved an accuracy of 97.40%, a mAP50 of 98.9%, and an inference time of 14.6 ms, outperforming Mask R-CNN in speed and mAP50. Mask R-CNN demonstrated an accuracy of 98.21%, a mAP50 of 95%, and an inference time of 88.7 ms, excelling in detailed segmentation tasks. Comparative analysis revealed no statistically significant differences in diagnostic performance between the models and experienced endodontists. <b>Conclusions:</b> Both YOLOv8 and Mask R-CNN demonstrated high diagnostic accuracy and reliability, comparable to experienced endodontists. YOLOv8's rapid detection capabilities make it particularly suitable for real-time clinical applications, while Mask R-CNN excels in precise segmentation. This study establishes a strong foundation for integrating AI into dental diagnostics, offering innovative solutions to improve clinical outcomes. Future research should address data diversity and explore multimodal imaging for enhanced diagnostic capabilities.