Machine learning-driven Heckmatt grading in facioscapulohumeral muscular dystrophy: A novel pathway for musculoskeletal ultrasound analysis.

Journal: Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology

Published Date: Feb 6, 2025

Abstract

OBJECTIVE: This study introduces a machine learning approach to automate muscle ultrasound analysis, aiming to improve objectivity and efficiency in segmentation, classification, and Heckmatt grading.

Authors

Francesco Marzola
Nens van Alfen

Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, Netherlands.
Jonne Doorduin
Kristen M Meiburger

Department of Electronics and Telecommunications, Politecnico di Torino, Italy.

Keywords

Adult Female Humans Machine Learning Male Middle Aged Muscle, Skeletal Muscular Dystrophy, Facioscapulohumeral Ultrasonography

External Resources

View on PubMed Access via DOI PubMed (40020544)

Machine learning-driven Heckmatt grading in facioscapulohumeral muscular dystrophy: A novel pathway for musculoskeletal ultrasound analysis.

Abstract

Authors

Keywords

External Resources

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