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Related Experiment Video

Updated: Jun 28, 2026

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
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Machine Learning Applications and Sarcopenia.

N Michalopoulos1, E Billis1, E Dermatas2

  • 1Department of Physiotherapy, School of Health Rehabilitation Science, University of Patras, Rio, Greece.

Advances in Experimental Medicine and Biology
|November 22, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) methods show high accuracy in detecting sarcopenia, a muscle-loss disease common in older adults. Key risk factors include age and chronic diseases, enabling early identification.

Keywords:
Artificial intelligenceMachine learningRisk factorsSarcopenia

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Area of Science:

  • Gerontology
  • Medical Informatics
  • Biomedical Engineering

Background:

  • Sarcopenia is a progressive age-related condition characterized by loss of muscle mass, strength, and function.
  • Early detection of sarcopenia is crucial for timely intervention and management in older adults.
  • The application of artificial intelligence (AI) and machine learning (ML) in healthcare is rapidly expanding.

Purpose of the Study:

  • To review machine learning methods for detecting individuals at risk of or suffering from sarcopenia.
  • To identify key risk factors associated with sarcopenia using ML approaches.
  • To assess the accuracy and effectiveness of various ML algorithms in sarcopenia prognosis.

Main Methods:

  • A systematic literature search was conducted on PubMed from July to August 2024.
  • Keywords included "sarcopenia," "artificial intelligence," "machine learning," and "risk factors."
  • Eleven studies involving 15,799 participants were included in the review.

Main Results:

  • All reviewed studies successfully identified sarcopenia with moderate to high accuracy using diverse ML methods.
  • Prominent ML methods included deep neural networks, LightGBM, Decision Tree, CatBoost, and k-nearest neighbors.
  • Significant risk factors identified were age, body mass index, waist circumference, chronic diseases, and socioeconomic features.

Conclusions:

  • Machine learning methods demonstrate significant potential for accurate and early detection of sarcopenia.
  • ML algorithms can extract valuable insights from data to predict sarcopenia risk.
  • Health professionals can utilize ML for faster, time-saving sarcopenia identification methods.