Screening/Diagnosing Sarcopenia with Machine Learning-Powered Risk Assessment: The SARCO X Study

Abstract

Objectives: Sarcopenia imposes significant morbidity and economic burden on health care systems, underscoring the critical need for early/effective screening and diagnosis. This study aimed to develop machine learning (ML)-based algorithm to facilitate the screening/diagnosis of sarcopenia. Design: A cross-sectional case-control study. Setting and Participants: This multicenter study enrolled subJects aged >= 45 years. Methods: Demographic data such as age, weight, height, education/exercise status, smoking, and co-morbid diseases were obtained. Sarcopenia was diagnosed using the basic and ML-based algorithms, which incorporate low quadriceps muscle mass/thickness, combined with prolonged chair stand test (CST) duration and/or reduced hand grip strength (HGS). Results: Of 5649 participants (1379 males, 24.4%), 1097 of them (19.4%) were sarcopenic. Using the ML-based model, significantly associated factors with sarcopenia were age, weight, height, education level, exercise status, and presence of hypertension and diabetes mellitus. Of the various ML models, the Gradient Boosting Classifier (GBC) demonstrated the highest performance in predicting sarcopenia in the holdout test data. For the ML-augmented algorithm, the recall value was 0.979; the precision value was 0.926, and the accuracy value was 0.980 for making the diagnosis of sarcopenia. When compared with the basic sarcopenia algorithm, the ML-augmented algorithm further decreased the need for HGS and ultrasound by 38.1% and 49.5%, respectively, demonstrating its effectiveness in optimizing sarcopenia diagnosis while minimizing testing required for medical device(s). Conclusions and Implications: The ML-based algorithm significantly reduces the need for testing/imaging in the diagnosis of sarcopenia. It facilitates the identification of sarcopenia particularly in the primary and secondary care settings and decreases the number of individuals who should be referred for further evaluation. (c) 2025 Post-Acute and Long-Term Care Medical Association.