## 4 Discussion
These results suggest that both models are capable of real-time detection, but YOLOv8n is more suitable when the computer's computational speed is not sufficient. These results suggest that AI technology can assist inexperienced examiners and contribute to the accurate detection of abnormalities in the examination of baseball elbow. In a prior investigation, we performed a binary classification task using images with OCD or images without OCD [21]. Images were captured from only two views, the anterior long and posterior long axes. Three DL models were compared, with accuracies of 0.818 in ResNet50, 0.841 in MobileNet_v2, and 0.872 in EfficientNet. In the present study, the accuracy of binary classification was 0.998 in the YOLOv8n-cls model, which is higher compared with the previous report. An object detection task was also performed in the prior report using the YOLOv2 model for OCD lesions. The YOLOv2 model successfully detected OCD with an average precision of 0.83 in the object detection task. However, when the YOLOv2 model was used to detect OCD lesions in the sports field, it showed a high false-positive rate due to its response to subcutaneous and muscle tissue when the standard view was not depicted. Therefore, it was considered necessary for inexperienced examiners to first visualize the standard view of the elbow joint. By detecting the anterior long axis, anterior short axis, posterior long axis, posterior short axis, and OCD region of the elbow joint by the AI model, inexperienced examiners could easily detect standard views and lesion areas. In the present study using the YOLOv8 model, the mAP (50) was 0.994 for the YOLOv8n model and 0.995 for the YOLOv8m model in the four standard views of the elbow joint and in the detection of OCD lesions. The mAP (50) was 0.998 for the YOLOv5n model and 0.993 for the YOLOv5m model. Although the dataset used in this study was slightly larger than those in previous studies, the model performance was better than in the previous report. Our results showed that the model could accurately detect both standard views of elbow joints and OCD lesions. Furthermore, visualization of the standard view prevents the false detection of OCD in extra-articular tissues such as subcutaneous tissue. Medical AI requires that the basis for the model's decisions be understandable to humans, called eXplainable AI (XAI). XAI has become an increasingly important field of research in recent years, and it promotes the formulation of explainability methods and provides a rationale that allows users to comprehend the results generated by AI systems. In the field of XAI for image data, overlaying image features such as GradientCAM and occlusion sensitivity as a heatmap is a common method. Our previous reports have shown that OCD lesion classification models capture features such as subchondral bone discontinuity and judge the lesions in a manner similar to human judgment [21]. Although occlusion sensitivity is an effective visualization tool due to its high spatial resolution and heat map accuracy, the computational complexity can cause issues with real-time display. To address this, we used a \"two-shot\" detection model that detects the standard view of elbow joints and OCD lesions. This method prevents false positives and can be easily reproduced by inexperienced examiners. Finally, we developed a GUI application running in a Python environment and connected it to a commercial laptop computer (Asus, ROG Zephyrus G14, CPU AMD ryzen7, GPU NVIDIA RTX3050) that was integrated with the US imaging system. The system operated at approximately 15 frames per second, making it suitable for real-time detection in medical examinations. There are several limitations to this study. The number of US data images is about 2000 from 40 cases, but we believe that more is needed in terms of the number of cases. In particular, since the dataset presented here is data from a single institution, it will be necessary to integrate data from other institutions in the future.