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Related Experiment Video

Updated: Sep 15, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Multi-scale machine learning model predicts muscle and functional disease progression.

Silvia S Blemker^1,2, Lara Riem³, Olivia DuCharme³

¹Springbok Analytics, 110 Old Preston Ave, Charlottesville, VA, 22902, USA. silvia.blemker@springbokanalytics.com.

Scientific Reports

|July 16, 2025

Summary

This summary is machine-generated.

Machine learning models predict disease progression in facioscapulohumeral muscular dystrophy (FSHD) using MRI and clinical data. This creates a digital twin for personalized patient assessment and improved clinical trials.

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Last Updated: Sep 15, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Assessing Functional Performance in the Mdx Mouse Model

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Published on: March 27, 2014

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

Area of Science:

Biomedical Engineering
Neurology
Data Science

Background:

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

Purpose of the Study:

To develop a multi-scale machine learning framework to predict regional, muscle, joint, and functional progression in FSHD.
To create a 'digital twin' of individual FSHD patients for clinical trial applications.

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 Timed Up-and-Go (TUG) performance.

Main Results:

Models demonstrated strong predictive performance on holdout datasets.
Predicted fat fraction change with RMSE of 2.16% and lean volume change with RMSE of 8.1 ml.
Predicted TUG change with RMSE of 0.6 s, with fat heterogeneity identified as a key predictor of muscle progression.

Conclusions:

Machine learning models integrating muscle and performance data can predict FSHD progression, addressing disease heterogeneity.
This approach has broad applicability for other neuromuscular diseases with similar variability.