## 4 Discussion
The results of the meta-analysis reported the acceptable precision and accuracy demonstrated by DL models in terms of MN localization and segmentation within the CTS [42]. In an effort to advance the state of knowledge, this study developed a CTS detection model using YOLOv5 as the base model. Using DL object detection models such as U-Net, R-CNN, and their derivatives, several studies have documented the automatic evaluation of the MN within the CTS with US imaging [27; 28; 29; 30; 43]. While two-step detectors such as R-CNN involve sequential processes, including the identification of candidate regions within an image where objects may reside, followed by classification, one-step detectors such as YOLO offer an alternative paradigm. YOLO directly predicts the bounding box around the target object and its class, allowing for real-time processing capabilities and increased computational efficiency [44]. The effectiveness of these DL models is typically measured by precision and recall, which are widely accepted evaluation metrics that measure the accuracy and robustness of the model. Previous studies have shown commendable results in terms of precision and recall. They showed a precision of 0.890 [41], 0.891 [43], 0.903 [29], 0.960 [30], and a recall of 0.960 [43], 0.912 [45], 0.903 [29], 0.980 [30]. In a previous study using the YOLO model for osteochondritis dissecans (OCD) of the elbow, the YOLOv2 model successfully detected OCD in the object detection task with an average precision of 0.83 [46]. The mAP (50) was 0.998 for the YOLOv5n model and 0.993 for the YOLOv5m model in another study that applied the YOLOv5 to OCD [47]. It is noteworthy that our developed model showed remarkably high values of both precision and recall. Therefore, it can be concluded that YOLO's object detection model performed creditably in the present study, confirming its effectiveness and relevance in the field of CTS diagnosis and evaluation. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Control Group** & **CTS Group** & \(p\)**-Value** \\ \hline [PERSON] et al. [25] & 2.45 (1.76) mm & 2.05 (2.82) mm & \textless{}0.01 \\ [PERSON] et al. [15] & 1.55 (1.04) mm & 0.89 (1.15) mm & \textgreater{}0.08 \\ [PERSON] et al. [23] & 0.84 (0.18) mm & 0.40 (0.13) mm & 0.021 \\ The present study & 3.56 (0.39) mm & 2.04 (0.21) mm & 0.0002 \\ \hline \hline \end{tabular} Data were expressed as the mean value (standard deviation). Abbreviation: CTS, carpal tunnel syndrome.  
\end{table} 
Table 2: The results of MN displacement at the radioulnar axis. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Control Group** & **CTS Group** & \(p\)**-Value** \\ \hline [PERSON] et al. [25] & 0.377 (0.399) mm & 0.069 (0.438) mm & 0.06 \\ [PERSON] et al. [23] & 0.64 (0.11) mm & 0.29 (0.08) mm & 0.015 \\ The present study & 1.06 (0.14) mm & 0.96 (0.29) mm & \textgreater{}0.05 \\ \hline \hline \end{tabular} Data were expressed as the mean value (standard deviation). Abbreviation: CTS, carpal tunnel syndrome.  
\end{table} 
Table 3: The results of MN displacement at the dorsopalmer axis. Among the reports on the potential of DL in relation to MN, only [PERSON] et al. reported on the morphological characteristics of MN during finger movement. Their investigation yielded an IoU of 0.83 using R-CNN [27], which showed the centroid offset, cross-sectional area, perimeter, and circularity of the MN. In the present study, by assessing MN velocity in addition to displacement during finger movement and comparing this with healthy subjects, we demonstrated that the application of a pre-trained DL model provides a novel way of assessing MN mobility reduction in CTS patients. This study builds on and extends previous studies by demonstrating the possibility of clinical applications of DL models in the diagnosis of CTS through the analysis of US imaging videos. Several limitations of this study should be acknowledged. First, [PERSON] et al.