Browse by author
Lookup NU author(s): Manish Bhardwaj, Dr Huizhi LiangORCiD, Ashwin Sivaharan, Dr Sandip Nandhra, Dr Tamer El-Sayed, Dr Varun OjhaORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Sarcopenia is a progressive loss of muscle mass and function linked to poor surgical outcomes such as prolonged hospital stays, impaired mobility, and increased mortality. Although it can be assessed through cross-sectional imaging by measuring skeletal muscle area (SMA), the process is time-consuming and adds to clinical workloads, limiting timely detection and management; however, this process could become more efficient and scalable with the assistance of artificial intelligence applications. This paper presents high-quality three-dimensional cross-sectional computed tomography (CT) images of patients with sarcopenia collected at the Freeman Hospital, Newcastle upon Tyne Hospitals NHS Foundation Trust. Expert clinicians manually annotated the SMA at the third lumbar vertebra, generating precise segmentation masks. We develop deep-learning models to measure SMA in CT images and automate this task. Our methodology employed transfer learning and self-supervised learning approaches using labelled and unlabeled CT scan datasets. While we developed qualitative assessment models for detecting sarcopenia, we observed that the quantitative assessment of SMA is more precise and informative. This approach also mitigates the issue of class imbalance and limited data availability. Our model predicted the SMA, on average, with an error of $\pm$3 percentage points against the manually measured SMA. The average dice similarity coefficient of the predicted masks was 93\%. Our results, therefore, show a pathway to full automation of sarcopenia assessment and detection.
Author(s): Bhardwaj M, Liang H, Sivaharan A, Nandhra S, Snasel V, El-Sayed T, Ojha V
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: The 11th Annual Conference on machine Learning, Optimization and Data science (LOD)
Year of Conference: 2025
Online publication date: 24/09/2025
Acceptance date: 01/06/2025
Date deposited: 13/08/2025
URL: https://lod2025.icas.events/
ePrints DOI: 10.57711/ftg3-1875
