ArticleScientific reports2024
AI driven analysis of MRI to measure health and disease progression in FSHD.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Sphingolipid Remodeling and Extracellular Vesicle Signatures Reflect Disease Severity in Facioscapulohumeral Dystrophy Driven by Mitochondrial Dysfunction and Endoplasmic Reticulum Stress.Antioxidants (Basel, Switzerland) · 2026Article
- MRI-Determined Muscle Fat Fractions and Contractile Volumes in Myotonic Dystrophy Type 2.Muscle & nerve · 2026Article
- Electrical impedance myography captures features of muscle structure measured by MRI and transcriptomic analysis in facioscapulohumeral muscular dystrophy.Journal of neuromuscular diseases · 2026Article
- Lower-Body Muscle Volumes Can Explain Half of the Variance in Sprint Speed Between Collegiate American Football Players.Scandinavian journal of medicine & science in sports · 2026Article
- Identification of KHDC1L, a DUX4-regulated protein, as a novel plasma biomarker in facioscapulohumeral muscular dystrophy.Human molecular genetics · 2026Article
- Emerging therapeutic strategies in muscular dystrophy: an updated review on pathogenesis and treatment advances.Molecular biology reports · 2026Review
- Intramuscular Fat as a Biomarker of Muscle Health: Strengths, Limitations and Open Challenges.Journal of cachexia, sarcopenia and muscle · 2026Review
- Intelligence Architectures and Machine Learning Applications in Contemporary Spine Care.Bioengineering (Basel, Switzerland) · 2025Review
- Multi-scale machine learning model predicts muscle and functional disease progression.Scientific reports · 2025Article
- Advances in Duchenne Muscular Dystrophy: Diagnostic Techniques and Dystrophin Domain Insights.International journal of molecular sciences · 2025Review
- Natural history of facioscapulohumeral muscular dystrophy evaluated by multiparametric quantitative MRI: a prospective cohort study.Journal of neurology · 2025Article
- 3D finite element models reveal regional fatty infiltration modulates tibialis anterior force generating capacity in FSHD.PloS one · 2025Article
- Age-related differences in intramuscular fat distribution: spatial quantification in human ankle plantar flexors.Frontiers in bioengineering and biotechnology · 2025Article
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
Facioscapulohumeral muscular dystrophy (FSHD) affects roughly 1 in 7500 individuals. While at the population level there is a general pattern of affected muscles, there is substantial heterogeneity in muscle expression across- and within-patients. There can also be substantial variation in the pattern of fat and water signal intensity within a single muscle. While quantifying individual muscles across their full length using magnetic resonance imaging (MRI) represents the optimal approach to follow disease progression and evaluate therapeutic response, the ability to automate this process has been limited. The goal of this work was to develop and optimize an artificial intelligence-based image segmentation approach to comprehensively measure muscle volume, fat fraction, fat fraction distribution, and elevated short-tau inversion recovery signal in the musculature of patients with FSHD. Intra-rater, inter-rater, and scan-rescan analyses demonstrated that the developed methods are robust and precise. Representative cases and derived metrics of volume, cross-sectional area, and 3D pixel-maps demonstrate unique intramuscular patterns of disease. Future work focuses on leveraging these AI methods to include upper body output and aggregating individual muscle data across studies to determine best-fit models for characterizing progression and monitoring therapeutic modulation of MRI biomarkers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.