## 1 Introduction
In particular, DL has been reported to accurately predict carpal tunnel syndrome by detecting image features in US images without measuring the median nerve cross-sectional area. The diagnostic accuracy was calculated from the confusion matrix obtained and showed high accuracy [10]. DL was also used on ultrasound images of Palmer 1B triangular fibro cartilage complex (TFCC) injuries. Classification of US images of injured TFCCs showed high accuracy comparable to MRI [11]. In these reports, AI models were trained using high-quality images called \"standard views\", which are suitable for US diagnosis. However, in clinical practice, it is often difficult for inexperienced clinicians to obtain a standard view. By applying DL to US imaging, inexperienced US examiners can receive instant feedback on scanned tissue identification, obtaining a good \"standard view\" and enabling faster and more standardized measurements. In this study, we focused on the YOLO model, a commonly utilized object-detection AI model. YOLO stands for \"You Only Look Once\" and came from a report on object detection by [PERSON] [12]. YOLO processes the entire image in one shot, simultaneously estimating object class and location for fast and accurate real-time object detection. [PERSON] has since released improved versions such as YOLOv2 and YOLOv3 to achieve even higher accuracy, and speed-improved versions have been released [13]. More recently, other researchers have released a series of improved versions, including YOLOX and YOLOv5. Several studies have utilized YOLO for medical image analysis. In 2021, [PERSON] et al. developed a computer-aided diagnostic system for breast cancer detection and classification [14]. The system detected masses on full-field digital mammograms with an average accuracy of 94.2% and accurately classified masses as benign or malignant with an accuracy of 84.6%. A transformer-based YOLO segmentation model for breast cancer mass detection and segmentation was reported in digital mammograms. The model achieved a 95.7% true positive rate and a 65.0% mean average accuracy in mass detection [15]. In 2022, [PERSON] et al. developed a YOLO deep-learning model for the detection and classification of primary bone tumors in full-field radiographs [16]. The model accurately detected bone lesions and classified radiographs into normal, benign, intermediate, and malignant types with an accuracy of 86.36% and 85.37% on internal and external validation sets, respectively. Thesestudies demonstrate the potential of YOLO as an effective tool for medical image analysis to provide an accurate and efficient diagnosis with limited human intervention. In this study, we used the YOLOv8 model announced by Ultralytics (Los Angels, CA, USA) in 2023. The YOLOv8 model is the latest model of YOLO that combines high speed and high accuracy. For the object detection task, we compared the YOLOv8 model with the previous model, YOLOv5, which is published by the same company. We hypothesized that AI technology could support technically inexperienced US examiners and be useful for \"Medical Checkup for Baseball Elbow\". In this study, we focused on the YOLO8 model, which is extremely fast and highly accurate. This model can perform image classification, object detection, and image segmentation tasks. We constructed an AI model based on images collected during a baseball-elbow checkup. The first aim of the study is to determine the diagnostic accuracy of YOLOv8 for US image classification of OCD or normal elbow-joint images--what we call \"standard view\". The second aim is to assess the diagnostic accuracy of object detection in OCD examples and in standard views of the elbow joints.