{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T16:23:21Z","timestamp":1787761401839,"version":"build-2784847793"},"update-to":[{"DOI":"10.12688\/f1000research.154680.1","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000}}],"reference-count":42,"publisher":"F1000 Research Ltd","license":[{"start":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Nil"}],"content-domain":{"domain":["f1000research.com"],"crossmark-restriction":false},"short-container-title":["F1000Res"],"abstract":"<ns3:p>Background Low back pain (LBP), the primary cause of disability, is the most common musculoskeletal disorder globally and the primary cause of disability. Magnetic resonance imaging (MRI) studies are inconclusive and less sensitive for identifying and classifying patients with LBP. Hence, this study aimed to investigate the role of artificial intelligence (AI) models in the prediction of LBP using T2 weighted MRI image of the lumbar spine. Methods This was a prospective case-control study. A total of 200 MRI patients (100 cases and controls each) referred for lumbar spine and whole spine screening were included. The scans were performed using 3.0 Tesla MRI (United Imaging Healthcare). T2 weighted images of the lumbar spine were segmented to extract radiomic features. Machine learning (ML) models, such as random forest, decision tree, logistic regression, K-nearest neighbors, adaboost, and deep learning methods (DL), such as ResNet and GoogleNet, were used, and performance measures were calculated. Results Our study showed that Random forest and AdaBoost are the most reliable ML models for predicting LBP. Random forest showed high performance with area under curve (AUC) values from 0.83 to 0.88 across all lumbar vertebrae and L2-L3, L3-L4, and L4-L5 intervertebral discs (IVDs), with AUCs of 0.88 the highest at L5-S1 IVD (0.92). Adaboost demonstrated high performance at the L2-L5 vertebrae with AUC values of 0.82 to 0.90, with the highest AUC (0.97) at the L5-S1 IVD. Among the DL models, GoogleNet outperformed the other models at 30 epochs with an accuracy of 0.85, followed by ResNet 18 (30 epochs) with an accuracy of 0.84. Conclusion The study demonstrated that ML and DL models can effectively predict LBP from MRI T2 weighted image of the lumbar spine. ML and DL models could also enhance the diagnostic accuracy of LBP, potentially leading to better patient management and outcomes.<\/ns3:p>","DOI":"10.12688\/f1000research.154680.2","type":"journal-article","created":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T08:25:21Z","timestamp":1728548721000},"page":"1035","update-policy":"https:\/\/doi.org\/10.12688\/f1000research.crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Role of Artificial intelligence model in prediction of low back pain using T2 weighted MRI of Lumbar spine"],"prefix":"10.12688","volume":"13","author":[{"given":"Ali","family":"Muhaimil","sequence":"first","affiliation":[{"name":"Department of Medical Imaging Technology, Manipal College of Health Professions Karnataka Manipal 576104 Manipal Academy of Higher Education"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7933-1192","authenticated-orcid":false,"given":"Saikiran","family":"Pendem","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging Technology, Manipal College of Health Professions Karnataka Manipal 576104 Manipal Academy of Higher Education"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3345-360X","authenticated-orcid":false,"given":"Niranjana","family":"Sampathilla","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Manipal Institute of Technology Manipal 576104 Manipal Academy of Higher Education"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7201-5733","authenticated-orcid":false,"given":"Priya","family":"P S","sequence":"additional","affiliation":[{"name":"Department of Radio Diagnosis and Imaging, Kasturba Medical College Karnataka Manipal 576104 Manipal Academy of Higher Education"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaushik","family":"Nayak","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging Technology, Manipal College of Health Professions Karnataka Manipal 576104 Manipal Academy of Higher Education"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krishnaraj","family":"Chadaga","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Manipal Institute of Technology Manipal 576104 Manipal Academy of Higher Education"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anushree","family":"Goswami","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Manipal Institute of Technology Manipal 576104 Manipal Academy of Higher Education"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5515-6377","authenticated-orcid":false,"given":"Obhuli Chandran","family":"M","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging Technology, Manipal College of Health Professions Karnataka Manipal 576104 Manipal Academy of Higher Education"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2726-1721","authenticated-orcid":false,"given":"Abhijith","family":"S","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging Technology, Manipal College of Health Professions Karnataka Manipal 576104 Manipal Academy of Higher Education"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2560","published-online":{"date-parts":[[2024,10,10]]},"reference":[{"key":"ref1","first-page":"e316-e329","article-title":"Global, regional, and national burden of low back pain, 1990-2020, its attributable risk factors, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021.","volume":"5","year":"2023","journal-title":"Lancet Rheumatol."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"1606299","DOI":"10.3389\/ijph.2024.1606299","article-title":"Global, Regional, and National Change Patterns in the Incidence of Low Back Pain From 1990 to 2019 and Its Predicted Level in the Next Decade.","volume":"69","author":"Y Gu","year":"2024 Feb","journal-title":"Int. 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