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

5 pages•Published: January 5, 2026

Hamidreza Rajabzadeh-Oghaz, Josie Elwell, François Boux de Casson, Sandrine Polakovic, Ashish Singh, Rakesh Raushan, Likitha Shetty, Bradley Schoch, William Aibinder, Bruno B. Gobbato, Christopher Roche and Vikas Kumar

Abstract

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 <1% between the ML-generated masks and the masks corrected by surgeons. The non-inferiority analysis demonstrated no significant difference between the generated masks to surgeons’ and inter-surgeon variations (p<0.05).

Keyphrases: computed tomography, computer vision, machine learning, shoulder arthroplasty

In: Joshua William Giles and Aziliz Guezou-Philippe (editors). Proceedings of The 25th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, vol 8, pages 134-138.

Links:	https://easychair.org/publications/paper/XzjR
	https://doi.org/10.29007/tx68

BibTeX entry

@inproceedings{CAOS2025:Expert_Validation_CT_Based,
  author    = {Hamidreza Rajabzadeh-Oghaz and Josie Elwell and François Boux de Casson and Sandrine Polakovic and Ashish Singh and Rakesh Raushan and Likitha Shetty and Bradley Schoch and William Aibinder and Bruno B. Gobbato and Christopher Roche and Vikas Kumar},
  title     = {Expert Validation of CT-Based Machine Learning Model for Segmentation and Quantification of Deltoid Muscles for Shoulder Arthroplasty},
  booktitle = {Proceedings of The 25th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery},
  editor    = {Joshua William Giles and Aziliz Guezou-Philippe},
  series    = {EPiC Series in Health Sciences},
  volume    = {8},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-5305},
  url       = {/publications/paper/XzjR},
  doi       = {10.29007/tx68},
  pages     = {134-138},
  year      = {2026}}

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