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영어

As the importance of artificial intelligence (AI) as a diagnostic aid in the medical field is gradually increasing, our study constructed an AI model that detects root canal treatment areas using oral and maxillofacial data. We constructed models using three types (v5s, v5l, v5x) of the real-time object detection algorithm YOLO (You Only Look Once) version 5 to meet the medical field's requirement for more precise, faster, and accurate performance. Each model was trained for 300 epochs using an SGD optimizer and as a result of the experiment, all versions of YOLO v5 algorithms showed high mAP@.5 performance over 0.93. However, for mAP@ .5:.95 performance which corresponds to more precise detection performance evaluation, it was confirmed that there is a difference in performance depending on the network size of the model. Thus, we suggest that YOLO v5x model with the largest network size is most suitable for detecting root canal treatment areas. Through this research, we suggest future research directions in fields related to development of diagnostic aids based on AI and look forward to developing more advanced object detection algorithms.