{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T04:12:12Z","timestamp":1788235932934,"version":"build-2803163510"},"reference-count":42,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T00:00:00Z","timestamp":1717718400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Italian Ministry of Health","award":["RC2024"],"award-info":[{"award-number":["RC2024"]}]},{"name":"Italian Ministry of Health","award":["2021-0612"],"award-info":[{"award-number":["2021-0612"]}]},{"name":"Fondazione Cariplo and Regione Lombardia","award":["RC2024"],"award-info":[{"award-number":["RC2024"]}]},{"name":"Fondazione Cariplo and Regione Lombardia","award":["2021-0612"],"award-info":[{"award-number":["2021-0612"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Muscular dystrophies present diagnostic challenges, requiring accurate classification for effective diagnosis and treatment. This study investigates the efficacy of deep learning methodologies in classifying these disorders using skeletal muscle MRI scans. Specifically, we assess the performance of the Swin Transformer (SwinT) architecture against traditional convolutional neural networks (CNNs) in distinguishing between healthy individuals, Becker muscular dystrophy (BMD), and limb\u2013girdle muscular Dystrophy type 2 (LGMD2) patients. Moreover, 3T MRI scans from a retrospective dataset of 75 scans (from 54 subjects) were utilized, with multiparametric protocols capturing various MRI contrasts, including T1-weighted and Dixon sequences. The dataset included 17 scans from healthy volunteers, 27 from BMD patients, and 31 from LGMD2 patients. SwinT and CNNs were trained and validated using a subset of the dataset, with the performance evaluated based on accuracy and F-score. Results indicate the superior accuracy of SwinT (0.96), particularly when employing fat fraction (FF) images as input; it served as a valuable parameter for enhancing classification accuracy. Despite limitations, including a modest cohort size, this study provides valuable insights into the application of AI-driven approaches for precise neuromuscular disorder classification, with potential implications for improving patient care.<\/jats:p>","DOI":"10.3390\/bioengineering11060580","type":"journal-article","created":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T10:43:42Z","timestamp":1717757022000},"page":"580","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Classification of Muscular Dystrophies from MR Images Improves Using the Swin Transformer Deep Learning Model"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3358-7111","authenticated-orcid":false,"given":"Alfonso","family":"Mastropietro","sequence":"first","affiliation":[{"name":"Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato, Consiglio Nazionale delle Ricerche, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicola","family":"Casali","sequence":"additional","affiliation":[{"name":"Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato, Consiglio Nazionale delle Ricerche, 20133 Milan, Italy"},{"name":"Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6996-7920","authenticated-orcid":false,"given":"Maria","family":"Taccogna","sequence":"additional","affiliation":[{"name":"Istituto di Tecnologie Biomediche, Consiglio Nazionale delle Ricerche, 20054 Segrate, Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1241-4350","authenticated-orcid":false,"given":"Maria","family":"D\u2019Angelo","sequence":"additional","affiliation":[{"name":"Unit of Rehabilitation of Rare Diseases of the Central and Peripheral Nervous System, Scientific Institute IRCCS Eugenio Medea, 23842 Bosisio Parini, Lecco, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6341-1304","authenticated-orcid":false,"given":"Giovanna","family":"Rizzo","sequence":"additional","affiliation":[{"name":"Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato, Consiglio Nazionale delle Ricerche, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9480-379X","authenticated-orcid":false,"given":"Denis","family":"Peruzzo","sequence":"additional","affiliation":[{"name":"Neuroimaging Unit, Scientific Institute IRCCS Eugenio Medea, 23842 Bosisio Parini, Lecco, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1016\/S0140-6736(02)07815-7","article-title":"The muscular dystrophies","volume":"359","author":"Emery","year":"2002","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1372","DOI":"10.1093\/ptj\/85.12.1372","article-title":"The muscular dystrophies: From genes to therapies","volume":"85","author":"Lovering","year":"2005","journal-title":"Phys. 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