## 3 Results  
Prior to the validation, one mask was excluded from the analysis because delineation of the deltoid was not possible due to the presence of hematoma. Aside from that one case, the validation was completed without issue. The time to perform the validation varied between surgeons, with surgeons A, B, and C completing their evaluations in 90 min, 120 min, and 180 min, respectively. There was 100% agreement among surgeons regarding the clinical acceptability ratings for each case. Table 3 summarizes the results of the expert shoulder surgeon qualitative validation, where surgeons A and C had a 95% acceptance rate and surgeon B had a 100% acceptance rate. In summary, 97% of the deltoid masks were deemed clinically acceptable and only one was rejected (deemed clinically unacceptable). Most ML-generated masks (81%) required only small/minor corrections. However, two ML-generated deltoid masks (6%) required a major correction, one of which was rejected. Figure 2 depicts the deltoid image analysis for two representative patients and reports the deltoid muscle size, shape parameters, volume, and fatty infiltration calculated from the ML-generated masks. For patient sample 1, the deltoid presented with low fatty infiltration and a large muscle volume. For patient sample 2, the deltoid presented with a substantial amount of fatty infiltration. Figure 3 depicts the ML-generated and surgeon-corrected masks for three samples: two requiring minor corrections and one requiring a major correction. For patient sample 3, the deltoid required a major correction, perhaps due to the high degree of fatty infiltration in the posterior deltoid (Figure 3). Table 4 reports the results of the quantitative assessment. For all cases, the median Dice coefficient was 1.0, indicating excellent similarity between ML-generated masks and the ground-truth, surgeon-corrected masks. The volume and fatty infiltration percentage errors were <1% in all cases. The only rejected case had a Dice coefficient of 0.75 and volume and fatty infiltration errors of 59% and 15%, respectively. The two cases that required major correction had Dice coefficients of 0.86, and volume and fatty infiltration errors of 27% and 8%, respectively. Model performance was also evaluated for different patient population and imaging parameters, which did not identify any substantial differences in any patient or imaging parameters as shown in Table 5.  
Figure 2: Deltoid segmentation and quantification analysis for two sample patients. Note that the yellow regions in the deltoid pictures represent fat (based on Hounsfield unit values ranging from \(-190\) to \(-30\)); whereas, the red regions in the deltoid pictures represent healthy muscle tissue (based on Hounsfield unit values ranging from \(-29\) to \(150\)). Patient 1 (**left**) has low fatty infiltration with a high deltoid volume. Patient 2 (**right**) has a lower deltoid volume and a higher degree of FI. The pipeline includes the deltoid characteristics of 783 shoulder arthroplasty patients, where the distribution of metrics can be used to gauge the characteristics of a given patient relative to a broader cohort. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Surgeon A** & **Surgeon B** & **Surgeon C** & **Total** \\ \hline Number of cases & 21 & 20 & 21 & 31 \\ \hline Accepted & 20 (95\%) & 20 (100\%) & 20 (95\%) & 30 (97\%) \\ \hline No correction needed & 9 (42.9\%) & 7 (35\%) & 5 (23.8\%) & 4 (13\%) \\ \hline Minor correction & 11 (52.4\%) & 12 (60\%) & 14 (66.7\%) & 25 (81\%) \\ \hline Major correction & 1 (4.8\%) & 1 (5\%) & 2 (9.5\%) & 2 (6\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the expert shoulder surgeon’s qualitative validation. Table 6 reports the non-inferiority results. The non-inferiority margin for Dice coefficient was 0.08. For the error metrics, the margins for the distance map, correction ratio, volume difference, and fat difference were 44%, 17%, 10%, and 3%, respectively.