Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2025
Machine learning-driven Heckmatt grading in facioscapulohumeral muscular dystrophy: A novel pathway for musculoskeletal ultrasound analysis.
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Marzola Francesco, van Alfen Nens, Doorduin Jonne, Meiburger Kristen M
Аннотация
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.
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.