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SarcoNet: A Pilot Study on Integrating Clinical and Kinematic Features for Sarcopenia Classification.

Muthamil Balakrishnan1, Janardanan Kumar2, Jaison Jacob Mathunny1

  • 1Department of Biomedical Engineering, SRM Institute of Science and Technology-Kattankulathur, Chengalpattu 603203, India.

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|October 16, 2025
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Summary

A new artificial neural network (ANN), SarcoNet, accurately identifies sarcopenia (age-related muscle loss) using clinical data and gait analysis. This AI tool shows promise for diagnosing sarcopenia, especially in resource-limited settings.

Keywords:
artificial Intelligencejoint anglesmachine learningmotion analysis

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Area of Science:

  • Gerontology
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Sarcopenia, an age-related decline in muscle mass and function, increases frailty and morbidity risks in older adults.
  • Early and accurate diagnosis of sarcopenia is crucial for timely intervention and management.

Purpose of the Study:

  • To design and evaluate SarcoNet, a novel artificial neural network (ANN) framework for classifying sarcopenic from non-sarcopenic individuals.
  • To assess the efficacy of integrating clinical parameters and kinetic gait features for sarcopenia detection.

Main Methods:

  • A pilot study involving 30 subjects classified as sarcopenic or non-sarcopenic.
  • Dataset comprised 31 clinical parameters (e.g., skeletal muscle mass) and 10 kinetic gait features from video analysis across varied terrains.
  • Performance comparison against Support Vector Machine (SVM), k-Nearest Neighbours (k-NN), Random Forest (RF), and ensemble classifiers.

Main Results:

  • SarcoNet achieved 94% classification accuracy, 100% specificity and precision, a 92.4% F1-score, and 0.94 AUC.
  • SarcoNet outperformed traditional machine learning models and ensemble methods.
  • Inclusion of lower-limb joint kinetics significantly improved the model's predictive power for sarcopenia.

Conclusions:

  • SarcoNet presents a viable AI-driven approach for sarcopenia diagnosis, particularly beneficial in low-resource healthcare environments.
  • Future research should focus on expanding the dataset, validating across diverse populations, and integrating explainable AI for clinical utility.