Expert Validation of CT-Based Machine Learning Model for Segmentation and Quantification of Deltoid Muscles for Shoulder Arthroplasty

The deltoid muscles play a crucial role in maintaining balanced arm function and enabling abduction following shoulder arthroplasty. Currently, pre-operative assessments of deltoid integrity rely primarily on visual inspection of medical images and subjective ratings. A recent work has shown accuracy of machine learning based pipeline to correctly segment and quantify characteristics of deltoid muscle in shoulder CT scans. In this paper, with the inputs from medical experts, we evaluated clinical acceptance and non-inferiority of the ML-based segmentations compared to the corrections provided by expert surgeons. The non-inferiority of the ML model was assessed by comparing model-generated masks to surgeons’ and inter-surgeon variations in metrics such as volume and fatty infiltration percentage. Expert validation showed 97% of masks to be clinically acceptable, with only 6% of ML generated masks requiring any major corrections. The median error in the volume and fatty infiltration measurements was