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Updated: May 20, 2025

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

Murat Kara1, Yasin Ceran2, Pelin Analay1

  • 1Department of Physical and Rehabilitation Medicine, Hacettepe University Medical School, Ankara, Turkey.

Journal of the American Medical Directors Association
|May 18, 2025
PubMed

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Summary
This summary is machine-generated.

A new machine learning (ML) algorithm aids sarcopenia screening by reducing the need for physical tests and imaging. This ML-based approach improves early detection and diagnosis of sarcopenia, especially in primary care settings.

Area of Science:

  • Gerontology
  • Biomedical Engineering
  • Artificial Intelligence in Healthcare

Background:

  • Sarcopenia presents a significant health burden, necessitating efficient screening and diagnostic tools.
  • Early identification of sarcopenia is crucial for managing associated morbidity and healthcare costs.

Purpose of the Study:

  • To develop and validate a machine learning (ML)-based algorithm for sarcopenia screening and diagnosis.
  • To enhance the accuracy and efficiency of sarcopenia diagnosis compared to traditional methods.

Main Methods:

  • A multicenter, cross-sectional case-control study involving participants aged 45 years and older.
  • Collected demographic and clinical data, diagnosing sarcopenia using basic and ML-based algorithms incorporating muscle mass, chair stand test (CST), and hand grip strength (HGS).
Keywords:
Quadriceps muscleartificial intelligencehand grip strengthhealth care costsultrasound

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  • Employed Gradient Boosting Classifier (GBC) for the ML model, evaluating its performance on holdout test data.

    • Main Results:

    • The ML-based model identified age, weight, height, education, exercise status, hypertension, and diabetes as significant factors associated with sarcopenia.
    • The Gradient Boosting Classifier (GBC) achieved high performance, with recall of 0.979, precision of 0.926, and accuracy of 0.980.
    • The ML-augmented algorithm reduced the need for HGS and ultrasound by 38.1% and 49.5%, respectively, optimizing diagnostic pathways.

    Conclusions:

    • The developed ML algorithm significantly decreases the requirement for extensive testing and imaging in sarcopenia diagnosis.
    • This tool facilitates earlier sarcopenia identification in primary and secondary care, reducing unnecessary referrals for further evaluation.