## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**MM**} & \multicolumn{2}{c}{**LM**} \\ \hline  & **DSC** & **Sensitivity** & **DSC** & **Sensitivity** \\ \hline  & \multicolumn{2}{c}{**External set**} \\ \hline Model 1 & 0.8891 (\(\pm\)0.0084) & 0.8956 (\(\pm\)0.0189) & 0.8782 (\(\pm\)0.0139) & 0.9511 (\(\pm\)0.0158) \\ \hline Model 2 & 0.7787 (\(\pm\)0.0673) & 0.7644 (\(\pm\)0.0741) & 0.7390 (\(\pm\)0.1084) & 0.8105 (\(\pm\)0.1218) \\ \hline Model 3 & 0.8535 (\(\pm\)0.0375) & 0.8548 (\(\pm\)0.0443) & 0.8673 (\(\pm\)0.0155) & 0.9555 (\(\pm\)0.0108) \\ \hline Model 4 & 0.8117 (\(\pm\)0.0477) & 0.7888 (\(\pm\)0.0604) & 0.8509 (\(\pm\)0.0270) & 0.9341 (\(\pm\)0.0385) \\ \hline Model 5 & 0.8175 (\(\pm\)0.0336) & 0.7880 (\(\pm\)0.0513) & 0.7147 (\(\pm\)0.1080) & 0.7775 (\(\pm\)0.1246) \\ \hline Method 1 & 0.8538 (\(\pm\)0.0382) & 0.8401 (\(\pm\)0.0460) & 0.8636 (\(\pm\)0.0236) & 0.9366 (\(\pm\)0.0363) \\ \hline Method 2 & 0.8852 (\(\pm\)0.0106) & 0.8695 (\(\pm\)0.0231) & 0.8854 (\(\pm\)0.0133) & 0.9439 (\(\pm\)0.0160) \\ \hline Method 3 & **0.9055 (\(\pm\)0.0069)** & **0.9008 (\(\pm\)0.0151)** & **0.8916 (\(\pm\)0.0128)** & **0.9534 (\(\pm\)0.0129)** \\ \hline \end{tabular} The dark color indicates the best performance of the comparison methods. MM: medial meniscus, LM: lateral meniscus.  
\end{table} 
Table 6: _Cont._  
\begin{table} \begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**Healthy**} & \multicolumn{2}{c}{**Tear**} \\ \hline  & **DSC** & **Sensitivity** & **DSC** & **Sensitivity** \\ \hline  & \multicolumn{2}{c}{**Test Set (Internal)**} \\ \hline Model 1 & 0.8713 (\(\pm\)0.0203) & 0.9541 (\(\pm\)0.0143) & 0.8830 (\(\pm\)0.0095) & 0.9359 (\(\pm\)0.0121) \\ \hline Model 2 & 0.7836 (\(\pm\)0.1006) & 0.8449 (\(\pm\)0.1197) & 0.7601 (\(\pm\)0.0603) & 0.7985 (\(\pm\)0.0710) \\ \hline Model 3 & 0.8210 (\(\pm\)0.0504) & 0.9022 (\(\pm\)0.0815) & 0.8519 (\(\pm\)0.0202) & 0.9080 (\(\pm\)0.0316) \\ \hline Model 4 & 0.8354 (\(\pm\)0.0508) & 0.9052 (\(\pm\)0.0761) & 0.8373 (\(\pm\)0.0318) & 0.8872 (\(\pm\)0.0399) \\ \hline Model 5 & 0.8257 (\(\pm\)0.0254) & 0.9341 (\(\pm\)0.0516) & 0.7753 (\(\pm\)0.0500) & 0.8089 (\(\pm\)0.0632) \\ \hline Method 1 & 0.8616 (\(\pm\)0.0168) & 0.9439 (\(\pm\)0.0298) & 0.8574 (\(\pm\)0.0228) & 0.8968 (\(\pm\)0.0341) \\ \hline Method 2 & 0.8502 (\(\pm\)0.0534) & 0.9001 (\(\pm\)0.0764) & 0.8808 (\(\pm\)0.0138) & 0.9116 (\(\pm\)0.0213) \\ \hline Method 3 & **0.8859 (\(\pm\)0.0178)** & **0.9556 (\(\pm\)0.0143)** & **0.9008 (\(\pm\)0.0077)** & **0.9443 (\(\pm\)0.0090)** \\ \hline \end{tabular} 
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**External set**} \\ \hline Model 1 & 0.8705 (\(\pm\)0.0202) & **0.9591 (\(\pm\)0.0120)** & 0.8891 (\(\pm\)0.0075) & 0.9042 (\(\pm\)0.0170) \\ \hline Model 2 & 0.7382 (\(\pm\)0.1379) & 0.8113 (\(\pm\)0.1573) & 0.7712 (\(\pm\)0.0640) & 0.7731 (\(\pm\)0.0707) \\ \hline Model 3 & 0.8541 (\(\pm\)0.0209) & 0.9567 (  
0.9995 (\(\pm\)0.0000) in both datasets. The results of these metrics show that the false positive rate is extremely low, and the overall accuracy of the model is high. In the Sensitivity metric, Method 3 outperformed the other methods with values of 0.9467 (\(\pm\)0.0077) on the test set and 0.9200 (\(\pm\)0.0119) on the external set. These results show that Method 3 can identify positive examples with high accuracy. It is seen that the methods built with multiple voting methods generally give better results than the models trained with YOLO. In model training, Model 1, which has the best performance, performs better than the other models. The performance analyses of the methods and models were not limited to general evaluations on the test set and external set; detailed analyses specific to the lateral and medial meniscus regions and healthy and torn meniscus conditions were also performed. In these analyses, DSC and Sensitivity metrics were used to emphasize the overall performance and Sensitivity of the model. These methods' regional and case-based success were compared based on these two metrics.