{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T01:13:46Z","timestamp":1790558026626,"version":"4.1.0"},"reference-count":63,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2024,5,10]],"date-time":"2024-05-10T00:00:00Z","timestamp":1715299200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["AC1550IF02"],"award-info":[{"award-number":["AC1550IF02"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012041","name":"Versus Arthritis","doi-asserted-by":"publisher","award":["CA20781"],"award-info":[{"award-number":["CA20781"]}],"id":[{"id":"10.13039\/501100012041","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Background<\/jats:title>\n<jats:p>Low back pain (LBP) is a major global disability contributor with profound health and socio-economic implications. The predominant form is non-specific LBP (NSLBP), lacking treatable pathology. Active physical interventions tailored to individual needs and capabilities are crucial for its management. However, the intricate nature of NSLBP and complexity of clinical classification systems necessitating extensive clinical training, hinder customised treatment access. Recent advancements in machine learning and computer vision demonstrate promise in characterising NSLBP altered movement patters through wearable sensors and optical motion capture. This study aimed to develop and evaluate a machine learning model (i.e., \u2019BACK-to-MOVE\u2019) for NSLBP classification trained with expert clinical classification, spinal motion data from a standard video alongside patient-reported outcome measures (PROMs).<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Methods<\/jats:title>\n<jats:p>Synchronised video and three-dimensional (3D) motion data was collected during forward spinal flexion from 83 NSLBP patients. Two physiotherapists independently classified them as motor control impairment (MCI) or movement impairment (MI), with conflicts resolved by a third expert. The Convolutional Neural Networks (CNNs) architecture, HigherHRNet, was chosen for effective pose estimation from video data. The model was validated against 3D motion data (subset of 62) and trained on the freely available MS-COCO dataset for feature extraction. The Back-to-Move classifier underwent fine-tuning through feed-forward neural networks using labelled examples from the training dataset. Evaluation utilised 5-fold cross-validation to assess accuracy, specificity, sensitivity, and F1 measure.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>Pose estimation\u2019s Mean Square Error of 0.35 degrees against 3D motion data demonstrated strong criterion validity. Back-to-Move proficiently differentiated MI and MCI classes, yielding 93.98% accuracy, 96.49% sensitivity (MI detection), 88.46% specificity (MCI detection), and an F1 measure of .957. Incorporating PROMs curtailed classifier performance (accuracy: 68.67%, sensitivity: 91.23%, specificity: 18.52%, F1: .800).<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusion<\/jats:title>\n<jats:p>This study is the first to demonstrate automated clinical classification of NSLBP using computer vision and machine learning with standard video data, achieving accuracy comparable to expert consensus. Automated classification of NSLBP based on altered movement patters video-recorded during routine clinical examination could expedite personalised NSLBP rehabilitation management, circumventing existing healthcare constraints. This advancement holds significant promise for patients and healthcare services alike.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0302899","type":"journal-article","created":{"date-parts":[[2024,5,10]],"date-time":"2024-05-10T17:23:30Z","timestamp":1715361810000},"page":"e0302899","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":15,"title":["BACK-to-MOVE: Machine learning and computer vision model automating clinical classification of non-specific low back pain for personalised management"],"prefix":"10.1371","volume":"19","author":[{"given":"Thomas","family":"Hartley","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yulia","family":"Hicks","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7635-4815","authenticated-orcid":true,"given":"Jennifer L.","family":"Davies","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dario","family":"Cazzola","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1502-764X","authenticated-orcid":true,"given":"Liba","family":"Sheeran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,5,10]]},"reference":[{"issue":"6","key":"pone.0302899.ref001","doi-asserted-by":"crossref","first-page":"2028","DOI":"10.1002\/art.34347","article-title":"A systematic review of the global prevalence of low back pain","volume":"64","author":"D Hoy","year":"2012","journal-title":"Arthritis Rheum"},{"issue":"1","key":"pone.0302899.ref002","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.berh.2006.08.004","article-title":"Low back pain (non-specific)","volume":"21","author":"M Krismer","year":"2007","journal-title":"Best Practice & Research Clinical Rheumatology"},{"issue":"10159","key":"pone.0302899.ref003","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1016\/S0140-6736(18)32279-7","article-title":"Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990\u20132017: a systematic analysis for the Global Burden of Disease Study 2017","volume":"392","author":"SL James","year":"2018","journal-title":"The Lancet"},{"issue":"10137","key":"pone.0302899.ref004","doi-asserted-by":"crossref","first-page":"2384","DOI":"10.1016\/S0140-6736(18)30488-4","article-title":"Low back pain: a call for action","volume":"391","author":"R Buchbinder","year":"2018","journal-title":"The Lancet"},{"issue":"10070","key":"pone.0302899.ref005","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1016\/S0140-6736(16)30970-9","article-title":"Non-specific low back pain","volume":"389","author":"C Maher","year":"2017","journal-title":"The Lancet"},{"key":"pone.0302899.ref006","unstructured":"NICE. 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