## 4 Discussion
[12] trained a CT-based Unet architecture to segment rotator cuff muscles and reported that the accuracy was comparable to that of human raters. [PERSON] et al. [14] proposed a framework to segment the supraspinatus muscle by leveraging the scapula as a landmark to locate the muscles. Additionally, [PERSON] et al. [7] proposed a semi-automatic CT-based framework for the segmentation of the deltoid and rotator cuff muscles. To our knowledge, our study is the first to present a fully automated CT-based ML framework for segmentation and radiomic quantification of the deltoid muscle. Future work will expand the scope of this tool to include other shoulder muscles and bones, by training algorithms for automated segmentation and radiomic quantification of the rotator cuff muscles and humeral and scapular bones in the shoulder. Additionally, we demonstrated the clinical application of this pipeline by using data from a large multi-institutional dataset to analyze the model performance across numerous different patient and image acquisition parameters, identifying no substantial differences across these parameters. Typically, ML-based image validation studies are performed using internal datasets and by quantifying errors using mathematical metrics, like the Dice coefficient, which may not necessarily reflect the clinical acceptability/viability of the segmented model [30; 31]. Our validation study advanced this effort by leveraging the knowledge and experience of multiple shoulder experts for whom the model was intended and by using external datasets generated separately from the development process to improve the generalizability and usability of the tool, which is necessary for successful adoption. Additionally, we leveraged a human-in-the-loop approach [32] to enhance the efficiency of model training during both the development and validation phases. Initialization of the deltoid masks during training increased the efficiency of annotators generating masks that served as model training data. [PERSON] et al. [33] reported that the use of initialized masks reduced the mask generation time by 80%. Initialization of the deltoid masks increased the efficiency of the external validation phase as well, as surgeon evaluation only required correction of the ML-generated masks, instead of starting from scratch with manual segmentation, which substantially reduced the overall time and effort required to perform the validation. Furthermore, the application of a human-in-the-loop approach can extend beyond model deployment, allowing model performance to continuously improve through active learning and ongoing feedback from users. Our validation study has several limitations. First, our training dataset contained slightly different distributions of age and gender than the validation dataset. However, our external validation did not show any significant difference in model performance between age and gender. Second, the validation dataset of 32 masks is relatively small. Despite the increased efficiency of correcting ML-generated masks, large scale studies of this nature remain challenging due to the time associated with generating expert curated ground-truth masks. Third, the use of initialized masks may introduce some anchoring bias, potentially skewing results, as greater differences between surgeons would be expected if the ground-truth were generated from scratch. Future efforts should focus on conducting larger scale expert validations incorporating continuous feedback monitoring with multiple iterations to ensure the robustness of the results. Fourth, the image database primarily consists of patients with bone-specific kernels, which restricts the generalizability of the model's performance to soft-tissue kernels. Additionally, our validation study included CT images from the most common kernels (i.e., Bone, BonePlus FC30, B60s, etc. ); further research is required to assess the performance of the auto-segmentation model on less common kernels and other imaging factors.