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Related Concept Videos

Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...

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Assessing Functional Performance in the Mdx Mouse Model
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Multi-scale machine learning model predicts muscle and functional disease progression in FSHD.

Silvia S Blemker, Lara Riem, Olivia DuCharme

    Biorxiv : the Preprint Server for Biology
    |March 3, 2025
    PubMed
    Summary

    This study developed a machine learning model using MRI and clinical data to predict disease progression in facioscapulohumeral muscular dystrophy (FSHD). The model accurately forecasts muscle changes and functional decline, aiding clinical trials for this genetic neuromuscular disorder.

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    Area of Science:

    • Biomedical Engineering
    • Neurology
    • Machine Learning

    Background:

    • Facioscapulohumeral muscular dystrophy (FSHD) is a heterogeneous genetic neuromuscular disorder with variable progression.
    • Current clinical metrics lack sensitivity for personalized assessment, hindering clinical trial design.

    Purpose of the Study:

    • To develop a multi-scale machine learning framework for predicting regional, muscle, joint, and functional progression in FSHD.
    • To create a 'digital twin' for individual FSHD patients to enhance clinical trials.

    Main Methods:

    • Integrated whole-body MRI-derived metrics (fat fraction, lean muscle volume, fat heterogeneity) with clinical and functional data from over 100 patients.
    • Developed a three-stage random forest model to predict annualized changes in muscle composition and the Timed Up-and-Go (TUG) test.

    Main Results:

    • Models demonstrated strong predictive performance on holdout datasets, predicting fat fraction change (RMSE 2.16%) and lean volume change (RMSE 8.1ml).
    • Fat heterogeneity within muscle was identified as a predictor of muscle-level progression.
    • The final model predicted TUG test changes with an RMSE of 0.6 seconds.

    Conclusions:

    • Machine learning models integrating muscle and performance data can effectively predict MRI-based disease progression and functional outcomes in FSHD.
    • This approach addresses the inherent heterogeneity and nonlinearity of FSHD, with potential broad applicability to other neuromuscular diseases.