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Related Experiment Video

Updated: May 23, 2026

Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography
07:33

Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography

Published on: November 8, 2024

Skeletal Muscle Mass Estimation from Lower Leg Digital Images Using Machine Learning.

Tomohiko Nagano1,2, Hiroo Matsuse3, Yoshio Takano4

  • 1Graduate School of Medicine, Kurume University, Kurume, Japan.

Progress in Rehabilitation Medicine
|May 22, 2026
PubMed
Summary

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This summary is machine-generated.

A new convolutional neural network (CNN) model accurately estimates skeletal muscle index (SMI) from lower leg images. This noninvasive method shows promise for efficient skeletal muscle mass screening.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Human Physiology

Background:

  • Skeletal muscle mass is crucial for overall health and mobility.
  • Accurate assessment of skeletal muscle index (SMI) is important for diagnosing and monitoring conditions related to muscle loss.
  • Current methods for SMI assessment can be invasive or time-consuming.

Purpose of the Study:

  • To develop and evaluate a convolutional neural network (CNN) model for estimating SMI from digital lower leg images.
  • To assess the predictive performance and accuracy of the developed CNN model.

Main Methods:

  • A CNN model was trained using digital images of the lower legs from 100 healthy adults (50 male, 50 female).
  • Data augmentation and Canny edge detection were applied to the images.
Keywords:
convolutional neural networklower leg imagessarcopenia

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Last Updated: May 23, 2026

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  • Model performance was validated using fivefold cross-validation, with 80% for training and 20% for testing.

    • Main Results:

    • The CNN model demonstrated strong predictive accuracy for SMI estimation.
    • Mean absolute percentage errors were below 5% for both lateral (3.70-4.15%) and posterior (4.23-4.85%) leg images.
    • Concordance correlation coefficients ranged from 0.90 to 0.94, indicating high agreement with reference values.

    Conclusions:

    • The developed CNN model accurately estimates SMI from lower leg digital images.
    • This noninvasive approach offers a practical and efficient tool for screening skeletal muscle mass.
    • The method has potential applications in clinical settings for early detection of sarcopenia and other muscle-related conditions.