## 4 Discussion  
The results of our expert shoulder surgeon validation objectively demonstrate the high clinical acceptability of the ML-based deltoid muscle auto-segmentation model analyzed in this study. These promising findings suggest that our CT image auto-segmentation and quantification framework can analyze pre-operative CT scans and better quantify the muscles of the glenohumeral joint, characterizing each patient's muscle size, shape, \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{3}{c}{**Surgeon A**} & \multicolumn{3}{c}{**Surgeon B**} & \multicolumn{3}{c}{**Surgeon C**} \\
**Errors** & **ML to** & **Inter-** & **Non** & **ML to** & **Inter-** & **Non** & **ML to** & **Inter-** & **Non** \\  & **Surgeon** & **Surgeon** & **In****Fay-Value** & **Surgeon** & **In****Fay-Value** & **Surgeon** & **In****Fay-Value** \\ \hline \hline Dice & 1.00 & 1.00 & & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ coefficient & [0.97, 1.00] & [0.98, 1.00] & \(p=0.001\) & [0.98, 1.00] & \(p<0.001\) & [0.98, 1.00] & \(p<0.001\) & [0.98, 1.00] & \(p<0.001\) \\ \hline Distance & 0.56 & 2.84 & 1.025 & 3.00 & 2.84 & 2.84 & 2.84 & 2.84 \\ \(\text{\small{\small{\small{\small{\small{\small{\small{\small{\small{\small{ \small{\small{\small{\small{\small{\small{\small{\leftleftleftleftleft({ 200,, 25} }\,1.\right)}\)\)\)\(\left\)\(\left\{\{0.001}\)}\)}}}}}}}}}}}} Error (\%) & 0.001 & & 7.24 & 7.24 & 1.00 & 2.88 & 2.84 & 2.84 & 2.84 \\ Error (\%) & [0.00, 9.01] & [0.00, 8.45] & \(p<0.001\) & [0.00, 7.24] & [0.00, 8.88] & \(p<0.001\) & [0.00, 6.25] & [0.00, 10.3] & \(p<0.001\) \\ \hline \hline Correction & 0.16 & 0.96 & & 0.30 & 0.725 & 0.001 & 0.8 & 0.65 & 1.00 & 1.00 & 1.00 \\ Ratio (\%) & [0.00, 6.26] & [0.00, 46] & \(p<0.001\) & [0.00, 3.345] & [0.00, 3.58] & \(p<0.001\) & [0.00, 3.31] & \(p<0.001\) \\ \hline Volume & 0.16 & 0.53 & & 0.20 & 0.39 & 0.79 & 0.30 & 1.00 & 1.00 \\ Diff (\%) & [0.00, 3.51] & [0.00, 3.77] & \(p<0.001\) & [0.00, 3.44] & [0.00, 1.98] & \(p<0.001\) & [0.00, 3.31] & [0.00, 1.85] & \(p<0.001\) \\ \hline FI & 0.04 & 0.12 & & 0.09 & 0.105 & & 0.06 & 0.08 & \\ Diff (\%) & [0.00, 3.52] & [0.00, 2.38] & \(p<0.001\) & [0.00, 1.42] & [0.00, 2.47] & \(p<0.001\) & [0.00, 2.19] & [0.00, 2.49] & \(p<0.001\) \\ \hline \end{tabular}
\end{table} 
Table 6: Results of the non-inferiority analysis. Note that all presented values represent the median followed by the 5 th and 95 th percentiles. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Category** & **Variable** & **\#** & **Dice** & **Correction** & **Distance-Map** & **Volume Diff** & **F1 Diff (\%)** \\ \hline All & & 32 (100\%) & 1.0 [0.97, 1.0] & 0.55 [0.0, 5.49] & 1.58 [0.0, 8.97] & 0.28 [0.0, 8.97] & 0.06 [0.0, 2.19] \\ \hline Gender & & Female & 17 (53\%) & 1.0 [0.87, 1.0] & 0.4 [0.0, 30.76] & 1.38 [0.0, 25.43] & 0.16 [0.0, 24.76] & 0.  
and quality. These insights can be used to optimize the preoperative planning process to consider more objective information related to the muscles in the shoulder when making treatment decisions related to shoulder arthroplasty [23; 24]. Our study offers several advances compared to previous image-based research in the shoulder. While some [6; 7; 8; 9; 10; 11; 12; 13; 14] have previously proposed ML-based methods for the segmentation and quantification of shoulder soft-tissues, very few of these studies have investigated the deltoid, and of those that have, most utilized MRI. MRI is a more expensive and time-consuming imaging modality than CT, and MRI is less relevant to this particular use-case since patients considering shoulder arthroplasty may not routinely undergo MRI. Additionally, the utilization of CT imaging is particularly advantageous for patients considering shoulder arthroplasty due to the widespread usage of CT-based pre-operative planning software [28; 29]. Only a few studies have utilized CT scans to auto-segment and quantify shoulder muscles [7; 12; 14]. [PERSON] et al.