## 4 Discussion  
In the examination of baseball elbow, the detection rate of abnormalities has been reported in several studies. The definition of abnormal findings is described in a previous report [17]. An irregularity, a break in the continuity of the high echogenic line of subchondoral bone, or a double line of the subchondoral bone of the capitellum were determined to be abnormal findings. A positive finding was declared when the aforementioned abnormalities were observed on the throwing side. In 2018, [PERSON] et al. reported a 1-3% detection rate in elementary through to high-school students [18]. Another report showed that 4% of children in medical checkups for baseball elbow showed abnormalities on an US and, among them, 50% showed abnormal findings upon X-ray examination [19]. In 2017, [PERSON] et al. reported the frequency of elbow OCD by baseball position. Among a total of 4249 participants, the overall prevalence of capitellar OCD diagnosed by US imaging was 2.2% (93 participants). As for playing positions, catchers had the highest prevalence of OCD (3.4%), followed by pitchers (2.5%). The prevalence for infielders and outfielders was 2.2% and 1.8%, respectively. There was no significant difference in the incidence of OCD by position [20]. These are the reports from a facility that performs frequent baseball-elbow examinations and has an expert in US examination. On the other hand, inexperienced US examiners may have difficulty detecting the standard view of elbow joints. Therefore, we hypothesized that AI technology could assist technically inexperienced examiners by helping with the detection of standard views, which could be useful for mass examination. Two tasks were performed in this study: image classification and object detection. First, we employed an image classification task using a standard view that accurately depicts the elbow joint. OCD lesions exhibit characteristic US images with features such as subchondoral bone irregularities or double lines, which can be easily recognized. The AI model used in this study was also able to classify the image with OCD lesions with high accuracy. Moreover, the model was able to classify the standard view of the elbow joint with high accuracy. The result indicates that the trained AI model can detect standard views of normal elbow joints and OCD lesions. Second, we used an object detection task. Several YOLO models were trained to detect the standard view of the elbow joint and OCD lesions in the object detection task. The YOLOv8n model showed an mAP (50) of 0.991 and  
Figure 5: (**a**) Detection of the anterior long view of the elbow joint and OCD lesion. (**b**) Screenshot of the GUI application designed for “Medical checkup for baseball elbow”. an mAP (50-95) of 0.784, while the YOLOv8m model showed an mAP (50) of 0.991 and an mAP (50-95) of 0.784. In the YOLOv5 model, the mAP (50) and mAP (50-95) were 0.998 and 0.666 for the YOLOv5n model, respectively, and 0.993 and 0.714 for the YOLOv5m model, respectively. The mAP values for the YOLOv8 models were higher than those for the YOLOv5 models. In general, a machine-learning model with good performance does not over-detect, even if the IOU threshold is reduced, and Precision remains high. The area under the Precision-Recall curve is sufficiently large for the model used in this study, indicating that the trained model showed good performance. The F-measure Confidence curve allows visualization of the thresholds that optimize Precision and Recall. In this study, the optimal value of F-measure Confidence was 0.701 for the YOLOv8n model and 0.781 for the YOLOv8m model, with higher confidence for the YOLOv8m model. Therefore, the YOLOv8m model is considered more suitable if the computer's computational speed is fast enough. On the other hand, the inference speed and FLOPs per image were 2.9 ms and 8.2 GFLOPs for the YOLOv8n model and 13.7 ms and 79.1 GFLOPs for the YOLOv8m model.