{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T02:01:43Z","timestamp":1792634503083,"version":"4.5.0"},"reference-count":32,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T00:00:00Z","timestamp":1739232000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Clinical Neurophysiology"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1016\/j.clinph.2025.01.016","type":"journal-article","created":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T12:45:32Z","timestamp":1738845932000},"page":"61-69","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":13,"special_numbering":"C","title":["Machine learning-driven Heckmatt grading in facioscapulohumeral muscular dystrophy: A novel pathway for musculoskeletal ultrasound analysis"],"prefix":"10.1016","volume":"172","author":[{"given":"Francesco","family":"Marzola","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nens","family":"van Alfen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonne","family":"Doorduin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristen M.","family":"Meiburger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.clinph.2025.01.016_b0005","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1109\/21.44046","article-title":"Textural features corresponding to textural properties","volume":"19","author":"Amadasun","year":"1989","journal-title":"IEEE Trans Syst, Man, Cybern"},{"key":"10.1016\/j.clinph.2025.01.016_b0010","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.1016\/j.clinph.2011.11.262","article-title":"Muscle ultrasonography: A diagnostic tool for amyotrophic lateral sclerosis","volume":"123","author":"Arts","year":"2012","journal-title":"Clin. Neurophysiol."},{"key":"10.1016\/j.clinph.2025.01.016_b0015","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.ultrasmedbio.2016.08.032","article-title":"Fully Automated Muscle Ultrasound Analysis (MUSA): Robust and Accurate Muscle Thickness Measurement","volume":"43","author":"Caresio","year":"2017","journal-title":"Ultrasound Med. Biol."},{"key":"10.1016\/j.clinph.2025.01.016_b0020","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco California USA: ACM; 2016, p. 785\u201394. https:\/\/doi.org\/10.1145\/2939672.2939785.","DOI":"10.1145\/2939672.2939785"},{"key":"10.1016\/j.clinph.2025.01.016_b0025","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/S0146-664X(75)80008-6","article-title":"Texture analysis using gray level run lengths","volume":"4","author":"Galloway","year":"1975","journal-title":"Comput. Graphics Image Process."},{"key":"10.1016\/j.clinph.2025.01.016_b0030","doi-asserted-by":"crossref","unstructured":"Haralick RM, Shanmugam K, Dinstein I. Textural Features for Image Classification. IEEE Trans Syst, Man, Cybern 1973;SMC-3:610\u201321. https:\/\/doi.org\/10.1109\/TSMC.1973.4309314.","DOI":"10.1109\/TSMC.1973.4309314"},{"key":"10.1016\/j.clinph.2025.01.016_b0035","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1016\/S0022-3476(82)80286-2","article-title":"Ultrasound imaging in the diagnosis of muscle disease","volume":"101","author":"Heckmatt","year":"1982","journal-title":"J. Pediatr."},{"key":"10.1016\/j.clinph.2025.01.016_b0040","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1002\/mus.27854","article-title":"Validity and reliability of visual assessment of orofacial muscle ultrasound images using a modified Heckmatt scale","volume":"68","author":"Lagarde","year":"2023","journal-title":"Muscle Nerve"},{"key":"10.1016\/j.clinph.2025.01.016_b0045","unstructured":"Lundberg S, Lee S-I. A Unified Approach to Interpreting Model Predictions 2017. https:\/\/doi.org\/10.48550\/ARXIV.1705.07874."},{"key":"10.1016\/j.clinph.2025.01.016_b0050","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1017\/cjn.2018.314","article-title":"Neuromuscular Ultrasound: Clinical Applications and Diagnostic Values","volume":"45","author":"Mah","year":"2018","journal-title":"Can J Neurol Sci"},{"key":"10.1016\/j.clinph.2025.01.016_b0055","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.ultrasmedbio.2017.09.013","article-title":"Monitoring Progression of Amyotrophic Lateral Sclerosis Using Ultrasound Morpho-Textural Muscle Biomarkers: A Pilot Study","volume":"44","author":"Mart\u00ednez-Pay\u00e1","year":"2018","journal-title":"Ultrasound Med. Biol."},{"key":"10.1016\/j.clinph.2025.01.016_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104623","article-title":"Deep learning segmentation of transverse musculoskeletal ultrasound images for neuromuscular disease assessment","volume":"135","author":"Marzola","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.clinph.2025.01.016_b0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.mad.2023.111860","article-title":"Assessment and risk prediction of frailty using texture-based muscle ultrasound image analysis and machine learning techniques","volume":"215","author":"Mir\u00f3n-Mombiela","year":"2023","journal-title":"Mech. Ageing Dev."},{"key":"10.1016\/j.clinph.2025.01.016_b0070","doi-asserted-by":"crossref","first-page":"2520","DOI":"10.1016\/j.ultrasmedbio.2015.04.021","article-title":"Advances in Quantitative Muscle Ultrasonography Using Texture Analysis of Ultrasound Images","volume":"41","author":"Molinari","year":"2015","journal-title":"Ultrasound Med. Biol."},{"key":"10.1016\/j.clinph.2025.01.016_b0075","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1002\/mus.21866","article-title":"New normal values for quantitative muscle ultrasound: Obesity increases muscle echo intensity","volume":"43","author":"Nijboer-Oosterveld","year":"2011","journal-title":"Muscle Nerve"},{"key":"10.1016\/j.clinph.2025.01.016_b0080","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1016\/j.ultrasmedbio.2020.12.012","article-title":"Muscle Composition Analysis of Ultrasound Images: A Narrative Review of Texture Analysis","volume":"47","author":"Paris","year":"2021","journal-title":"Ultrasound Med. Biol."},{"key":"10.1016\/j.clinph.2025.01.016_b0085","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.clinph.2025.01.016_b0090","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1002\/mus.24613","article-title":"Muscle ultrasound from diagnostic tool to outcome measure\u2014Quantification is the challenge","volume":"52","author":"Pillen","year":"2015","journal-title":"Muscle Nerve"},{"key":"10.1016\/j.clinph.2025.01.016_b0095","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.ultrasmedbio.2022.09.011","article-title":"Hamstring Muscle Quality Properties Using Texture Analysis of Ultrasound Images","volume":"49","author":"Sahinis","year":"2023","journal-title":"Ultrasound Med. Biol."},{"key":"10.1016\/j.clinph.2025.01.016_b0100","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1016\/j.ultrasmedbio.2018.11.012","article-title":"Transverse Muscle Ultrasound Analysis (TRAMA): Robust and Accurate Segmentation of Muscle Cross-Sectional Area","volume":"45","author":"Salvi","year":"2019","journal-title":"Ultrasound Med. Biol."},{"key":"10.1016\/j.clinph.2025.01.016_b0105","doi-asserted-by":"crossref","DOI":"10.1002\/ima.22984","article-title":"cyto\u2010Knet\u00a0: An instance segmentation approach for multiple myeloma plasma cells using conditional kernels","volume":"34","author":"Salvi","year":"2024","journal-title":"Int J Imaging Syst Tech"},{"key":"10.1016\/j.clinph.2025.01.016_b0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.ultras.2023.106940","article-title":"Texture analysis of ultrasound images obtained with different beamforming techniques and dynamic ranges \u2013 A robustness study","volume":"131","author":"Seoni","year":"2023","journal-title":"Ultrasonics"},{"key":"10.1016\/j.clinph.2025.01.016_b0115","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/0146-664X(82)90093-4","article-title":"Neighboring gray level dependence matrix for texture classification","volume":"20","author":"Sun","year":"1982","journal-title":"Comput. Graphics Image Process."},{"key":"10.1016\/j.clinph.2025.01.016_b0120","series-title":"Application to cell nuclei classification","article-title":"Texture indexes and gray level size zone matrix","author":"Thibault","year":"2009"},{"key":"10.1016\/j.clinph.2025.01.016_b0125","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1017\/cjn.2018.269","article-title":"Neuromuscular Ultrasound: A New Tool in Your Toolbox","volume":"45","author":"Van Alfen","year":"2018","journal-title":"Can J Neurol Sci"},{"key":"10.1016\/j.clinph.2025.01.016_b0130","doi-asserted-by":"crossref","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","article-title":"Computational Radiomics System to Decode the Radiographic Phenotype","volume":"77","author":"Van Griethuysen","year":"2017","journal-title":"Cancer Res."},{"key":"10.1016\/j.clinph.2025.01.016_b0135","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1016\/j.nmd.2023.10.015","article-title":"Establishing the role of muscle ultrasound as an imaging biomarker in facioscapulohumeral muscular dystrophy","volume":"33","author":"Vincenten","year":"2023","journal-title":"Neuromuscul. Disord."},{"key":"10.1016\/j.clinph.2025.01.016_b0140","doi-asserted-by":"crossref","unstructured":"Vincenten SCC, Voermans NC, Cameron D, Van Engelen BGM, Van Alfen N, Mul K. The complementary use of muscle ultrasound and MRI in FSHD: Early versus later disease stage follow-up. Clinical Neurophysiology 2024:S1388245724000646. https:\/\/doi.org\/10.1016\/j.clinph.2024.02.036.","DOI":"10.1016\/j.clinph.2024.02.036"},{"key":"10.1016\/j.clinph.2025.01.016_b0145","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.7863\/ultra.16.07054","article-title":"Quantitative Sonographic Assessment of the Quadriceps Femoris Muscle in Healthy Japanese Adults: Quantitative Sonographic Assessment of the Quadriceps Femoris Muscle","volume":"36","author":"Watanabe","year":"2017","journal-title":"J Ultrasound Med"},{"key":"10.1016\/j.clinph.2025.01.016_b0150","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.nmd.2024.07.001","article-title":"Improving Heckmatt muscle ultrasound grading scale through Rasch analysis","volume":"42","author":"Wijntjes","year":"2024","journal-title":"Neuromuscul. Disord."},{"key":"10.1016\/j.clinph.2025.01.016_b0155","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1002\/mus.27669","article-title":"Visual versus quantitative analysis of muscle ultrasound in neuromuscular disease","volume":"66","author":"Wijntjes","year":"2022","journal-title":"Muscle Nerve"},{"key":"10.1016\/j.clinph.2025.01.016_b0160","unstructured":"Zhang W, Pang J, Chen K, Loy CC. K-Net: Towards Unified Image Segmentation 2021. https:\/\/doi.org\/10.48550\/ARXIV.2106.14855."}],"container-title":["Clinical Neurophysiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1388245725000367?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1388245725000367?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T02:50:06Z","timestamp":1743043806000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1388245725000367"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":32,"alternative-id":["S1388245725000367"],"URL":"https:\/\/doi.org\/10.1016\/j.clinph.2025.01.016","relation":{},"ISSN":["1388-2457"],"issn-type":[{"value":"1388-2457","type":"print"}],"subject":[],"published":{"date-parts":[[2025,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Machine learning-driven Heckmatt grading in facioscapulohumeral muscular dystrophy: A novel pathway for musculoskeletal ultrasound analysis","name":"articletitle","label":"Article Title"},{"value":"Clinical Neurophysiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.clinph.2025.01.016","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Authors. Published by Elsevier B.V. on behalf of International Federation of Clinical Neurophysiology.","name":"copyright","label":"Copyright"}]}}