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

Aug 4, 2026

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This study introduces a machine learning approach to automate muscle ultrasound analysis, aiming to improve objectivity and efficiency in segmentation, classification, and Heckmatt grading. A dataset of 25,005 B-mode images from 290 participants (including 110 FSHD patients) was analyzed using a standardized protocol. Manual segmentation and observer Heckmatt grading served as ground truth. K-Net performed joint segmentation and classification, while texture-derived radiomics features were scored with XGBoost and explained via SHAP. Results showed strong segmentation and classification performance (IoU 73.40–74.03) and high Heckmatt AUC across classes, supporting clinical decision-making.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Introduction

Muscle ultrasound and neuromuscular disease diagnosis

Heckmatt scale and limitations of visual grading

Study abbreviations and context

Methods

Data set and imaging protocol

Manual ground truth

K-Net segmentation and classification

Radiomics, XGBoost scoring, and SHAP explainability

Results

Segmentation and classification accuracy (IoU)

Heckmatt grading performance (AUC)

Key image features for visual scoring

Conclusion

Automatic pipeline for quantitative Heckmatt grading

POLITECNICO DI TORINO Repository ISTITUZIONALE Machine learning-driven Heckmatt grading in facioscapulohumeral muscular dystrophy: A novel pathway for musculoskeletal ultrasound analysis Original Machine learning-driven Heckmatt grading in facioscapulohumeral muscular dystrophy: A novel pathway formusculoskeletal ultrasound analysis / Marzola, Francesco; van Alfen, Nens; Doorduin, Jonne; Meiburger, Kristen M.. -In: CLINICAL NEUROPHYSIOLOGY. -ISSN 1388-2457. -172:(2025), pp. 61-69. [10 . 1016/j.clinph.2025.01.016] Availability: This version is available at: 11583/2998708 since: 2025-04-01T07:04:51Z Publisher: Elsevier Published DOI:10.1016/j.clinph.2025.01.016 Terms of use: This article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository Publisher copyright (Article begins on next page) 04 October 2025 Clinical Neurophysiology 172 (2025) 61–69 Contents lists available at ScienceDirect Clinical Neurophysiology journal [homepage: www.elsevier.com/locate/clinph](homepage: www.elsevier.com/locate/clinph) | Machine learning-driven Heckmatt grading in facioscapulohumeral muscular dystrophy: A novel pathway for musculoskeletal ultrasound analysis | | | | | --- | --- | --- | --- | | Francesco Marzola a,*, Nens van Alfenb, Jonne Doorduinc, Kristen M. Meiburger a
a Biolab, PolitoBIOMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin Italy
b Department of Neurology, Clinical Neuromuscular Imaging Group, Donders Institute for Brain Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
c Department of Intensive Care Medicine, Radboud University Medical Center, Nijmegen, The Netherlands | | | | | A R T I C L E I N F O | | A B S T R A C T | | | Keywords:
Muscle ultrasound Machine learning Muscle segmentation Heckmatt grading
Neuromuscular disease diagnosis | | 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.
Methods: We analyzed a dataset of 25,005 B-mode images from 290 participants (110 FSHD patients) acquired using a single Esaote ultrasound scanner with a standardized protocol. Manual segmentation and Heckmatt grading by experienced observers served as ground truth. K-Net was utilized for simultaneous muscle segmentation and classification. Heckmatt scoring was approached with texture analysis, using a modified scale with three classes (Normal, Uncertain, Abnormal). Radiomics features were extracted using PyRadiomics and automatic scoring was performed using XGBoost, incorporating explainability through SHAP analysis.
Results: K-Net demonstrated high accuracy in skeletal muscle classification and segmentation, with Intersection over Union ranging from 73.40 to 74.03 across folds. Heckmatt’s grading achieved an Area Under Curve of 0.95, 0.87, and 0.97 for classes Normal, Uncertain, and Abnormal. SHAP analysis highlighted histogram-based features as critical for visual scoring.
Conclusion: This study proposes and validates an automatic pipeline for muscle ultrasound analysis, leveraging machine learning for segmentation, classification, and quantitative Heckmatt grading.
Significance: Automating the visual assessment of muscle ultrasound images improves the objectivity and efficiency of muscle ultrasound, supporting clinical decision-making. | | 1. Introduction Muscle ultrasound (MUS) using B-mode imaging has emerged as a valuable tool to study the morphology and composition of muscle. Its capacity to evaluate various properties of muscle tissue, not limited to echogenicity but also including muscle texture changes, atrophy, vascularization, and elasticity, is beneficial in diagnosing neuromuscular diseases (Mah and van Alfen, 2018). Furthermore, it offers the advantage of visu

FAQs

What is the main goal of the machine learning approach in this study?

To automate muscle ultrasound analysis by improving objectivity and efficiency for segmentation, classification, and quantitative Heckmatt grading.

How was the model trained and evaluated?

Using 25,005 B-mode images from 290 participants with manual segmentation and observer Heckmatt grading as ground truth, alongside radiomics features scored by XGBoost with SHAP explainability.

What were the reported performance results for segmentation and Heckmatt grading?

K-Net achieved an IoU range of 73.40–74.03 across folds, and Heckmatt grading reached AUC values of 0.95, 0.87, and 0.97 for Normal, Uncertain, and Abnormal classes.

Aug 4, 2026

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