## 2 Materials and Methods  
### Data Collection  
A total of 44 cases included those who were found to have OCD at medical checkups and those who were treated for OCD at the authors' institution. Normal data were obtained from US images of the contralateral elbow joint or from cases in which no abnormalities were found at the medical checkup. Movies were obtained from four directions: anterior short (AS) axis and anterior long (AL) axis in elbow extension, and posterior short (PS) axis and posterior long (PL) axis in elbow maximum flexion (Figure 1). The movie was recorded at 30 frames per second while the 15 or 18 MHz liner US probe (Arietta prologue, FUJIFILM, Tokyo, Japan) was placed in the anterior or posterior center of the elbow-joint surface to delineate the standard view. To increase image variation, the probe was slowly tilted and slid against the surface of the elbow joint while capturing the movies. A total of 2430 images were generated from the movies by cropping the region of interest and capturing the images from the movies. A breakdown of the dataset is shown in Table 1. Using this dataset, we trained the YOLOv8 model to perform two tasks: image classification and object detection. The images were resized into 640 \(\times\) 640 and augmented using Albumentations (version 1.0.3), which is a Python library for image augmentation. The parameters for image augmentation were the following: Blur (probability of applying the transform; \(p\) = 0.01), MedianBlur (\(p\) = 0.01), ToGray (\(p\) = 0.01), Contrast Limited Adaptive Histogram Equalization (\(p\) = 0.01), RandomBrightnessContrast (\(p\) = 0.01), RandomGamma (\(p\) = 0.01), and ImageCompression (\(p\) = 0.01).  
The first task was to classify images with OCD lesions. Two classification tasks were used in the classification task. The first classification task was a two-class classification into \"with OCD\" and \"without OCD\". The second classification task was to classify images with a normal elbow joint (standard view). This was a multi-class classification of four standard views: AS axis, AL axis, PS axis, and PL axis from the obtained image dataset. The definition of each class is as follows: AS--region showing the humeral trochlea and capitellum; AL--region showing the round shape of the humeral capitellum and radial head; PS view--area showing the outline of the ulnar olecranon and humeral capitellum; PL view--area showing the round shape of humeral capitellum and radial head; OCD--the irregular surface of the cartilage and the interruption or double line of the subchondral bone line which has high echogenicity (Figures 2 and 3).  
Figure 1: Elbow position during US image data collection: **(a)** anterior long view; **(b)** anterior short view; **(c)** posterior long view; **(d)** posterior short view.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Normal** & **OCD** & **Total** \\ \hline Anterior long & 439 & 270 & 709 \\ Anterior short & 278 & 256 & 534 \\ Posterior long & 425 & 362 & 787 \\ Posterior short & 200 & 200 & 400 \\ Total & 1342 & 1088 & 2430 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Breakdown of elbow US image dataset.  
Figure 2: Representative data of the standard view of a normal US image: **(a)** anterior long view; **(b)** anterior short view; **(c)** posterior long view; **(d)** posterior short view.  
The second task was an object detection task. The annotation tool LabelImg (ver. 1.8.1) was used for labeling the US image. Annotation was performed manually by one orthopedic surgeon skilled in US examinations. Bounding boxes were set up for a total of five classes of regions to detect standard views of the elbow joint and OCD lesions.