{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T15:40:59Z","timestamp":1747150859970,"version":"3.40.5"},"reference-count":68,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2020,8,9]],"date-time":"2020-08-09T00:00:00Z","timestamp":1596931200000},"content-version":"am","delay-in-days":347,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#am"},{"start":{"date-parts":[[2019,8,28]],"date-time":"2019-08-28T00:00:00Z","timestamp":1566950400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1606136"],"award-info":[{"award-number":["1606136"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008982","name":"Qatar National Research Fund","doi-asserted-by":"publisher","award":["NPRP8\u20101606\u20103\u2010322","NPRP8\u2010293\u20102\u2010124"],"award-info":[{"award-number":["NPRP8\u20101606\u20103\u2010322","NPRP8\u2010293\u20102\u2010124"]}],"id":[{"id":"10.13039\/100008982","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Muscle and Nerve"],"published-print":{"date-parts":[[2019,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Introduction<\/jats:title><jats:p>Golden retriever muscular dystrophy (GRMD) is a spontaneous X\u2010linked canine model of Duchenne muscular dystrophy that resembles the human condition. Muscle percentage index (MPI) is proposed as an imaging biomarker of disease severity in GRMD.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>To assess MPI, we used MRI data acquired from nine GRMD samples using a 4.7\u2009T small\u2010bore scanner. A machine learning approach was used with eight raw quantitative mapping of MRI data images (T1m, T2m, two Dixon maps, and four diffusion tensor imaging maps), three types of texture descriptors (local binary pattern, gray\u2010level co\u2010occurrence matrix, gray\u2010level run\u2010length matrix), and a gradient descriptor (histogram of oriented gradients).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The confusion matrix, averaged over all samples, showed 93.5% of muscle pixels classified correctly. The classification, optimized in a leave\u2010one\u2010out cross\u2010validation, provided an average accuracy of 80% with a discrepancy in overestimation for young (8%) and old (20%) dogs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>MPI could be useful for quantifying GRMD severity, but careful interpretation is needed for severe cases.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mus.26657","type":"journal-article","created":{"date-parts":[[2019,8,9]],"date-time":"2019-08-09T15:34:05Z","timestamp":1565364845000},"page":"621-628","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Muscle percentage index as a marker of disease severity in golden retriever muscular dystrophy"],"prefix":"10.1002","volume":"60","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9414-9986","authenticated-orcid":false,"given":"Aydin","family":"Eresen","sequence":"first","affiliation":[{"name":"Department of 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