## 3 Results
### Object Detection  
The mAPs, speeds, and FLOPs of the models trained in this study are shown in Table 4. The detection accuracy comparing the bounding box detected by the trained model with the true-label bounding box annotated by the physician was 0.994 for mAP (50) and 0.787 for mAP (50-95) for the YOLOv8n model and 0.995 for mAP (50) and 0.782 for mAP (50-95) for the YOLOv8m model. The inference speed and FLOPs per image  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{3}{c}{**Predicted Label**} \\ \cline{3-4}  & & **Normal** & **OCD** \\ \hline \multirow{3}{*}{True Label} & Normal & 477 & 0 \\ \cline{2-4}  & OCD & 1 & 393 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix of binary classification.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Predicted Label**} \\ \cline{3-5}  & & **AL** & **AS** & **PL** & **PS** \\ \hline \multirow{3}{*}{True Label} & AL & 137 & 0 & 6 & 0 \\ \cline{2-5}  & AS & 0 & 109 & 0 & 0 \\ \cline{1-1} \cline{2-5}  & PL & 0 & 0 & 158 & 0 \\ \cline{1-1} \cline{2-5}  & PS & 0 & 0 & 0 & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix of multiclass classification to classify the standard view of the elbow joint.  
were 2.9 ms and 8.2 GFLOPs for the YOLOv8n model and 13.7 ms and 79.1 GFLOPs for the YOLOv8m model. The mAP (50), mAP (50-95), speed, and FLOPS were 0.998, 0.666, 8.2, and 4.1 for the YOLOv5n model and 0.993, 0.714, 12.4.47.9 for the YOLOv5m model, respectively. The YOLOv8 model outperformed the YOLOv5 model in both detection accuracies. The Precision-Recall curves and F-measure Confidence curves are shown in Figure 3. The F-measure Confidence curve can visualize the Confidence that optimizes Precision and Recall. Normally, a higher Confidence is considered better. In this study, the optimal value of F-measure Confidence was 0.701 for the YOLOv8n model and 0.781 for the YOLOv8m model. The larger the AUC of the Precision-Recall curve, the better-performing the machine-learning model is. In this study, the AUC of the Precision-Recall curve for the YOLOv8n model was 0.994 and 0.995 for the YOLOv8m model (Figure 4).  
An example of detection performed on a video of an actual OCD case and the appearance of the GUI application are shown. By using the object detection model, it was possible to detect standard views and OCD areas with high accuracy (Figure 5).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Parameters** & \multirow{2}{*}{**mAP(50)**} & \multirow{2}{*}{**mAP(50–95)**} & **Speed** & **FLOPs** \\  & **(M)** & & & **(ms/pic)** & **(G)** \\ \hline YOLOv8n & 3.0 & 0.994 & 0.787 & 2.9 & 8.2 \\ \hline YOLOv8m & 25.8 & 0.995 & 0.782 & 13.7 & 79.1 \\ \hline YOLOv5n & 1.8 & 0.988 & 0.666 & 8.2 & 4.1 \\ \hline YOLOv5m & 20.8 & 0.993 & 0.714 & 12.4 & 47.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Size of parameters and Performance scores of two object detection models.  
Figure 4: F-measure Confidence curve and Precision–Recall curve (**a**) F-measure Confidence curve of YOLOv8n. (**b**) F-measure Confidence curve for YOLOv8m. (**c**) Precision–Recall curve for YOLOv8n. (**d**) Precision–Recall curve for YOLOv8m.