## 3 Results
### Comparison between Dominant and Non-Dominant Side
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Most Discriminant Features for Side**} & \multicolumn{2}{c}{**Most Discriminant Features for Side**} & \multicolumn{2}{c}{**Most Discriminant Features for**} \\  & **Determination (Only Healthy** & \multicolumn{2}{c}{**Subclinical Tendiopathy**} \\  & & **Subjects) (Weight)** & \multicolumn{2}{c}{**Determination (Weight)**} \\ \hline Kurtosis & (\(-\)65.0) & H. Homogeneity (45\({}^{\circ}\)) & (172.0) & H. Symmetry (0\({}^{\circ}\)) & (\(-\)24.5) \\ H. Correlation (0\({}^{\circ}\)) & (58.9) & H. Contrast (45\({}^{\circ}\)) & (\(-\)155.8) & H. Contrast (45\({}^{\circ}\)) & (\(-\)14.3) \\ H. Contrast (0\({}^{\circ}\)) & (12.9) & H. Symmetry (45\({}^{\circ}\)) & (\(-\)48.7) & H. Entropy (0\({}^{\circ}\)) & (14.2) \\ Skewness & (\(-\)12.3) & H. Symmetry (0\({}^{\circ}\)) & (\(-\)29.7) & H. Correlation (45\({}^{\circ}\)) & (13.4) \\ H. Entropy (0\({}^{\circ}\)) & (9.6) & H. Energy (0\({}^{\circ}\)) & (27.2) & H. Homogeneity (0\({}^{\circ}\)) & (5.5) \\ H. Correlation (45\({}^{\circ}\)) & (7.0) & H. Correlation (0\({}^{\circ}\)) & (27.1) & Mean Intensity & (4.8) \\ H. Energy (45\({}^{\circ}\)) & (\(-\)6.3) & H. Correlation (135\({}^{\circ}\)) & (6.1) & First\(-\)order Entropy & (4.3) \\ H. Homogeneity (0\({}^{\circ}\)) & (3.5) & H. Energy (45\({}^{\circ}\)) & (\(-\)5.3) & Kurtosis & (\(-\)4.2) \\ H. Entropy (45\({}^{\circ}\)) & (2.3) & Mean Intensity & (5.1) & Variance & (3.0) \\ H. Symmetry (45\({}^{\circ}\)) & (\(-\)2.2) & H. Entropy (45\({}^{\circ}\)) & (4.8) & H. Correlation (0\({}^{\circ}\)) & (2.9) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Image features that were the most discriminant between dominant and non-dominant side (all subjects and only healthy subjects) and to determine subclinical tendiopathy. The second column of Table 4 lists the texture features that had the highest absolute weight on the first canonical variable, meaning that they were the most discriminant for the determination of the side. The ROC analysis gave an area under the curve (AUC) equal to 0.989 (Figure 3d). Considering an optimal threshold as the one determined by the maximum Youden index, the final classification results gave sensitivity, specificity, and accuracy all equal to 96.3%. Figure 3: Results obtained when comparing dominant and non-dominant side texture features considering all subjects (**first row**) and only subjects with normal tendon structure (**second row**). The black circles represent the dominant side textures features, while the gray circles represent the non-dominant side texture features. (**a**) Representation of all subjects in the plane of the first two canonical variables obtained by a MANOVA analysis. The vertical gray dotted line represents the optimal threshold used (maximum Youden index) to obtain the sensitivity (86.9%), specificity (79.8%), and accuracy (83.3%) results; (**b**) ROC analysis results demonstrating the classification quality of the first MANOVA canonical variable, with an AUC equal to 0.906; (**c**) Representation of the healthy subjects in the plane of the first two canonical variables obtained by a MANOVA analysis. The vertical gray dotted line represents the optimal threshold used (maximum Youden index) to obtain the sensitivity (96.3%), specificity (96.3%), and accuracy (96.3%) results; (**d**) ROC analysis results demonstrating the classification quality of the first MANOVA canonical variable, with an AUC equal to 0.989.