Deep Learning-Assisted Detection of Sarcopenia in Cross-Sectional Computed Tomography Imaging

2025-08-24

AI Summary

Clinical screening for sarcopenia is hindered by the inefficiency and high labor burden of manual skeletal muscle area (SMA) measurement on conventional CT images. To address this, we propose a 3D CT image segmentation framework that synergistically integrates transfer learning and self-supervised learning for fully automated, accurate skeletal muscle segmentation and quantitative assessment. The method effectively mitigates challenges of limited annotated data and severe class imbalance, achieving an SMA prediction mean absolute error of ±3 percentage points and a muscle segmentation Dice coefficient of 93%, while preserving clinical interpretability. Notably, this work is the first to jointly incorporate self-supervised pretraining and task-specific fine-tuning within a single model architecture—enhancing generalizability and deployment efficiency. Our approach provides a robust, non-invasive, scalable, and early-screening solution for sarcopenia in clinical practice.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Semi-Supervised Learning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Large pretrained models with web data
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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 +-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.

Problem

• Automating sarcopenia detection in CT scans
• Reducing manual muscle measurement workload
• Addressing class imbalance and data scarcity

Innovation

• Deep learning automates muscle area measurement
• Transfer learning enhances CT scan analysis
• Self-supervised learning addresses data limitations