{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T06:54:49Z","timestamp":1790924089921,"version":"4.1.0"},"reference-count":39,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2020,3,16]],"date-time":"2020-03-16T00:00:00Z","timestamp":1584316800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Ultrason Imaging"],"published-print":{"date-parts":[[2020,5]]},"abstract":"<jats:p>Fibromyalgia (FM) diagnosis remains a challenge for clinicians due to a lack of objective diagnostic tools. One proposed solution is the use of quantitative ultrasound (US) techniques, such as image texture analysis, which has demonstrated discriminatory capabilities with other chronic pain conditions. From this, we propose the use of image texture variables to construct and compare two machine learning models (support vector machine [SVM] and logistic regression) for differentiating between the trapezius muscle in healthy and FM patients. US videos of the right and left trapezius muscle were acquired from healthy ( n = 51) participants and those with FM ( n = 57). The videos were converted into 64,800 skeletal muscle regions of interest (ROIs) using MATLAB. The ROIs were filtered by an algorithm using the complex wavelet structural similarity index (CW-SSIM), which removed ROIs that were similar. Thirty-one texture variables were extracted from the ROIs, which were then used in nested cross-validation to construct SVM and elastic net regularized logistic regression models. The generalized performance accuracy of both models was estimated and confirmed with a final validation on a holdout test set. The predicted generalized performance accuracy of the SVM and logistic regression models was computed to be 83.9 \u00b1 2.6% and 65.8 \u00b1 1.7%, respectively. The models achieved accuracies of 84.1%, and 66.0% on the final holdout test set, validating performance estimates. Although both machine learning models differentiate between healthy trapezius muscle and that of patients with FM, only the SVM model demonstrated clinically relevant performance levels.<\/jats:p>","DOI":"10.1177\/0161734620908789","type":"journal-article","created":{"date-parts":[[2020,3,16]],"date-time":"2020-03-16T06:20:34Z","timestamp":1584339634000},"page":"135-147","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":12,"title":["Machine Learning Diagnostic Modeling for Classifying Fibromyalgia Using B-mode Ultrasound Images"],"prefix":"10.1177","volume":"42","author":[{"given":"Michael","family":"Behr","sequence":"first","affiliation":[{"name":"Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada"},{"name":"Division of Physical Medicine & Rehabilitation, Department of Medicine, University of Toronto, Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saba","family":"Saiel","sequence":"additional","affiliation":[{"name":"Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada"},{"name":"Division of Physical Medicine & Rehabilitation, Department of Medicine, University of Toronto, Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6518-1541","authenticated-orcid":false,"given":"Valerie","family":"Evans","sequence":"additional","affiliation":[{"name":"Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada"},{"name":"Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3889-7557","authenticated-orcid":false,"given":"Dinesh","family":"Kumbhare","sequence":"additional","affiliation":[{"name":"Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada"},{"name":"Division of Physical Medicine & Rehabilitation, Department of Medicine, University of Toronto, Toronto, ON, Canada"},{"name":"Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2020,3,16]]},"reference":[{"key":"bibr1-0161734620908789","doi-asserted-by":"publisher","DOI":"10.1002\/art.1780380104"},{"key":"bibr2-0161734620908789","doi-asserted-by":"publisher","DOI":"10.1093\/pm\/pnx155"},{"key":"bibr3-0161734620908789","doi-asserted-by":"publisher","DOI":"10.1111\/papr.12302"},{"key":"bibr4-0161734620908789","doi-asserted-by":"publisher","DOI":"10.1016\/j.semarthrit.2016.08.012"},{"key":"bibr5-0161734620908789","doi-asserted-by":"publisher","DOI":"10.3899\/jrheum.170095"},{"key":"bibr6-0161734620908789","volume-title":"Guidance for Industry and FDA Staff Qualification Process for Drug Development Tools","author":"U.S. Department of Health &amp; Human Services, U.S. Food &amp; Drug Administration, Centre for Drug Evaluation and Research","year":"2014"},{"key":"bibr7-0161734620908789","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.1880030418"},{"issue":"3","key":"bibr8-0161734620908789","first-page":"319","volume":"30","author":"Meenagh G","year":"2012","journal-title":"Clin Exp Rheumatol"},{"key":"bibr9-0161734620908789","doi-asserted-by":"publisher","DOI":"10.1002\/art.1780400114"},{"key":"bibr10-0161734620908789","doi-asserted-by":"publisher","DOI":"10.1615\/CritRevBiomedEng.2017024947"},{"issue":"1","key":"bibr11-0161734620908789","first-page":"161","volume":"7","author":"Yang W","year":"2012","journal-title":"J Comp"},{"key":"bibr12-0161734620908789","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2005.00503.x"},{"issue":"1","key":"bibr13-0161734620908789","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"Tibshirani R","year":"1996","journal-title":"J Roy Stat Soc B Met"},{"key":"bibr14-0161734620908789","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.1970.10488634"},{"key":"bibr15-0161734620908789","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3"},{"issue":"4","key":"bibr16-0161734620908789","first-page":"373","volume":"79","author":"World Medical Association","year":"2001","journal-title":"B World Health Organ"},{"key":"bibr17-0161734620908789","author":"MathWorks","journal-title":"Image Processing Toolbox, Release 2018a"},{"key":"bibr18-0161734620908789","doi-asserted-by":"publisher","DOI":"10.1002\/jum.14909"},{"key":"bibr19-0161734620908789","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2009.2025923"},{"key":"bibr20-0161734620908789","doi-asserted-by":"publisher","DOI":"10.1177\/0161734619839980"},{"key":"bibr21-0161734620908789","doi-asserted-by":"publisher","DOI":"10.1016\/S0301-5629(00)00302-1"},{"key":"bibr22-0161734620908789","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1973.4309314"},{"key":"bibr23-0161734620908789","doi-asserted-by":"publisher","DOI":"10.1109\/PROC.1979.11328"},{"issue":"2","key":"bibr24-0161734620908789","first-page":"172","volume":"4","author":"Galloway MM","year":"1975","journal-title":"Comput Vision Graph"},{"key":"bibr25-0161734620908789","volume-title":"Statistics and Machine Learning Toolbox, Release 2018a","author":"MathWorks"},{"key":"bibr26-0161734620908789","volume-title":"The Elements of Statistical Learning","author":"Hastie T","year":"2008","edition":"2"},{"key":"bibr27-0161734620908789","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511801389"},{"key":"bibr28-0161734620908789","first-page":"1889","volume":"6","author":"Fan RE","year":"2005","journal-title":"J Mach Learn Res"},{"key":"bibr29-0161734620908789","doi-asserted-by":"publisher","DOI":"10.1090\/S0002-9947-1950-0051437-7"},{"key":"bibr30-0161734620908789","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinimag.2012.09.024"},{"key":"bibr31-0161734620908789","doi-asserted-by":"publisher","DOI":"10.1504\/IJCONVC.2013.054658"},{"key":"bibr32-0161734620908789","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2010.02.067"},{"key":"bibr33-0161734620908789","first-page":"e430","volume":"61","author":"Ahmed S","year":"2018","journal-title":"Ann Phys Rehabil Med"},{"issue":"3","key":"bibr34-0161734620908789","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1111\/j.2517-6161.1995.tb02046.x","volume":"57","author":"Glonek GF","year":"1995","journal-title":"J Roy Stat Soc B Met"},{"key":"bibr35-0161734620908789","unstructured":"Bishop CM. Pattern Recognition and Machine Learning. New York: Springer-Verlag; 2006."},{"key":"bibr36-0161734620908789","first-page":"352","volume":"1996","author":"Sarle WS","journal-title":"Comput Sci Stat"},{"key":"bibr37-0161734620908789","doi-asserted-by":"publisher","DOI":"10.1177\/1759720X17740076"},{"key":"bibr38-0161734620908789","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultrasmedbio.2015.04.021"},{"key":"bibr39-0161734620908789","doi-asserted-by":"publisher","DOI":"10.1186\/1742-7622-2-11"}],"container-title":["Ultrasonic Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0161734620908789","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/0161734620908789","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0161734620908789","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T16:28:27Z","timestamp":1777652907000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/0161734620908789"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,16]]},"references-count":39,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,5]]}},"alternative-id":["10.1177\/0161734620908789"],"URL":"https:\/\/doi.org\/10.1177\/0161734620908789","relation":{},"ISSN":["0161-7346","1096-0910"],"issn-type":[{"value":"0161-7346","type":"print"},{"value":"1096-0910","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,16]]}}}