{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T18:02:07Z","timestamp":1785261727769,"version":"3.55.0"},"reference-count":93,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:00:00Z","timestamp":1785196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/publicdomain\/zero\/1.0\/"}],"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>Artificial intelligence (AI) is increasingly used to enhance diagnostic accuracy, automate image interpretation, and support clinical decision-making. In the field of spine care, applications include MRI and CT-based detection of lumbar disc degeneration, spinal stenosis, vertebral fractures, and axial spondyloarthritis, as well as emerging symptom-based and multimodal diagnostic tools. However, evidence remains dispersed across modalities and conditions, and the quality and clinical readiness of AI systems vary. This scoping review maps current AI applications for diagnosing spinal disorders and identifies gaps for future research and clinical translation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This review followed Joanna Briggs Institute (JBI) and PRISMA-ScR guidelines. Ovid MEDLINE, AMED, Embase, Cochrane CENTRAL, Web of Science, and Scopus were searched from January 2019 to December 2024. Eligible studies were mapped according to AI methodology, diagnostic target, data source, and validation approach, and were required to involve human participants, include sufficient methodological detail, and published in English peer-reviewed journals. No geographic restrictions were applied. Data was extracted on study design, AI methodology, diagnostic target, validation approach, and usability. Methodological quality was assessed using a 19-point scoring system covering study design, reporting clarity, data validation, and feature selection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Forty-six studies met the inclusion criteria, conducted primarily in Asia and Europe, with two studies from North America and one from South America. Most investigations were retrospective, imaging-based deep learning models applied to MRI or CT for detecting disc herniation, lumbar spinal stenosis, modic changes, vertebral fractures, and sacroiliitis. Several studies used prospective designs or external validation. Diagnostic performance was generally high across imaging models, with many studies describing accuracy that approached or matched clinician benchmarks, particularly in sacroiliitis classification, disc disease detection, and stenosis grading. Methodological scores ranged from 7.5 to 17.5 out of 19, with recurrent weaknesses in handling missing data, feature selection, and data element validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This review maps a growing body of literature on AI applications for diagnosing spinal disorders, with studies most frequently reporting favorable performance for MRI- and CT-based detection of degenerative and inflammatory conditions. Evidence remains preliminary and heterogeneous.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0352200","type":"journal-article","created":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T17:41:48Z","timestamp":1785260508000},"page":"e0352200","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence in spine care: A scoping review of diagnostic applications"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7473-0350","authenticated-orcid":true,"given":"Victoria A.","family":"Bensel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1975-1468","authenticated-orcid":true,"given":"Anne","family":"Habeck","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcda Hilaire","family":"Brunot","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eleni-James","family":"Becton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Monika","family":"Ray","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandria L.","family":"Brackett","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anthony J.","family":"Lisi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,7,28]]},"reference":[{"issue":"6","key":"pone.0352200.ref001","doi-asserted-by":"crossref","first-page":"299","DOI":"10.21037\/atm.2020.02.175","article-title":"Global low back pain prevalence and years lived with disability from 1990 to 2017: estimates from the Global Burden of Disease Study 2017","volume":"8","author":"A Wu","year":"2020","journal-title":"Ann Transl Med"},{"key":"pone.0352200.ref002","doi-asserted-by":"crossref","first-page":"1469298","DOI":"10.3389\/fmed.2024.1469298","article-title":"The burden, trends, and projections of low back pain attributable to high body mass index globally: an analysis of the global burden of disease study from 1990 to 2021 and projections to 2050","volume":"11","author":"C Zhang","year":"2024","journal-title":"Front Med (Lausanne)"},{"issue":"2","key":"pone.0352200.ref003","doi-asserted-by":"crossref","first-page":"487","DOI":"10.14245\/ns.2448372.186","article-title":"Comparative review of the socioeconomic burden of lower back pain in the United States and globally","volume":"21","author":"D Chang","year":"2024","journal-title":"Neurospine"},{"issue":"3","key":"pone.0352200.ref004","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.spmd.2004.08.003","article-title":"Differential diagnosis of low back pain","volume":"2","author":"E Huntoon","year":"2004","journal-title":"Semin Pain Med"},{"key":"pone.0352200.ref005","doi-asserted-by":"crossref","first-page":"3051","DOI":"10.2147\/JMDH.S512254","article-title":"Diagnostic challenges and patient safety: the critical role of accuracy - a systematic review","volume":"18","author":"TAF Alharbi","year":"2025","journal-title":"J Multidiscip Healthc"},{"issue":"10","key":"pone.0352200.ref006","doi-asserted-by":"crossref","first-page":"3581","DOI":"10.3390\/jcm12103581","article-title":"Diagnosis value of patient evaluation components applicable in primary care settings for the diagnosis of low back pain: a scoping review of systematic Reviews","volume":"12","author":"J Mathieu","year":"2023","journal-title":"J Clin Med"},{"issue":"7","key":"pone.0352200.ref007","doi-asserted-by":"crossref","first-page":"3947","DOI":"10.21037\/qims-2022-04","article-title":"The value of magnetic resonance imaging and computed tomography in the study of spinal disorders","volume":"12","author":"F Ruiz Santiago","year":"2022","journal-title":"Quant Imaging Med Surg"},{"key":"pone.0352200.ref008","doi-asserted-by":"crossref","first-page":"1298054","DOI":"10.3389\/fradi.2025.1298054","article-title":"The advancement and utility of multimodal imaging in the diagnosis of degenerative disc disease","volume":"5","author":"EM Teichner","year":"2025","journal-title":"Front Radiol"},{"issue":"6","key":"pone.0352200.ref009","doi-asserted-by":"crossref","first-page":"1845","DOI":"10.1148\/rg.2018180021","article-title":"Fundamentals of diagnostic error in imaging","volume":"38","author":"JN Itri","year":"2018","journal-title":"Radiographics"},{"issue":"10","key":"pone.0352200.ref010","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1016\/j.diii.2020.09.001","article-title":"Interobserver agreement issues in radiology","volume":"101","author":"M Benchoufi","year":"2020","journal-title":"Diagn Interv Imaging"},{"issue":"1134","key":"pone.0352200.ref011","doi-asserted-by":"crossref","first-page":"20210995","DOI":"10.1259\/bjr.20210995","article-title":"Interobserver variation in the interpretation of magnetic resonance enterography in Crohn\u2019s disease","volume":"95","author":"G Bhatnagar","year":"2022","journal-title":"Br J Radiol"},{"issue":"4","key":"pone.0352200.ref012","doi-asserted-by":"crossref","first-page":"811","DOI":"10.3174\/ajnr.A4173","article-title":"Systematic literature review of imaging features of spinal degeneration in asymptomatic populations","volume":"36","author":"W Brinjikji","year":"2015","journal-title":"AJNR Am J Neuroradiol"},{"issue":"3","key":"pone.0352200.ref013","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.diii.2014.02.019","article-title":"A review of the value of MRI signs in low back pain","volume":"96","author":"I Ract","year":"2015","journal-title":"Diagn Interv Imaging"},{"key":"pone.0352200.ref014","doi-asserted-by":"crossref","first-page":"111032","DOI":"10.1016\/j.ejrad.2023.111032","article-title":"Work overload and diagnostic errors in radiology","volume":"167","author":"\u00d6 Kasalak","year":"2023","journal-title":"Eur J Radiol"},{"issue":"1","key":"pone.0352200.ref015","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.jacr.2024.08.030","article-title":"Seven challenges in radiology practice: from declining reimbursement to inadequate labor force: summary of the 2023 ACR intersociety meeting","volume":"22","author":"B Siewert","year":"2025","journal-title":"J Am Coll Radiol"},{"issue":"1","key":"pone.0352200.ref016","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.physio.2014.04.006","article-title":"Comparison of referrals for lumbar spine magnetic resonance imaging from physiotherapists, primary care and secondary care: how should referral pathways be optimised?","volume":"101","author":"V Parmar","year":"2015","journal-title":"Physiotherapy"},{"key":"pone.0352200.ref017","doi-asserted-by":"crossref","first-page":"3455","DOI":"10.2147\/JPR.S488133","article-title":"From pain to progress: comprehensive analysis of musculoskeletal disorders worldwide","volume":"17","author":"J Zhou","year":"2024","journal-title":"J Pain Res"},{"key":"pone.0352200.ref018","doi-asserted-by":"crossref","first-page":"1562701","DOI":"10.3389\/fpubh.2025.1562701","article-title":"Global, regional, and national burden of musculoskeletal disorders, 1990\u20132021: an analysis of the global burden of disease study 2021 and forecast to 2035","volume":"13","author":"M Liu","year":"2025","journal-title":"Front Public Health"},{"issue":"5","key":"pone.0352200.ref019","doi-asserted-by":"crossref","first-page":"102132","DOI":"10.1016\/j.xcrm.2025.102132","article-title":"AI-powered integration of multimodal imaging in precision medicine for neuropsychiatric disorders","volume":"6","author":"W Huang","year":"2025","journal-title":"Cell Rep Med"},{"issue":"7","key":"pone.0352200.ref020","first-page":"591","article-title":"Multimodal artificial intelligence in medical diagnostics","volume":"16","author":"B Jandoubi","year":"2025","journal-title":"Inf"},{"issue":"9","key":"pone.0352200.ref021","doi-asserted-by":"crossref","first-page":"983","DOI":"10.3390\/jpm14090983","article-title":"Artificial intelligence-driven diagnostic processes and comprehensive multimodal models in pain medicine","volume":"14","author":"M Cascella","year":"2024","journal-title":"J Personal Med"},{"issue":"18","key":"pone.0352200.ref022","doi-asserted-by":"crossref","first-page":"11708","DOI":"10.3390\/ijerph191811708","article-title":"Artificial intelligence in spinal imaging: current status and future directions","volume":"19","author":"Y Cui","year":"2022","journal-title":"Int J Environ Res Public Health"},{"issue":"3","key":"pone.0352200.ref023","doi-asserted-by":"crossref","first-page":"100760","DOI":"10.1016\/j.jorep.2025.100760","article-title":"Artificial intelligence for lumbar spine anatomy and pathology detection: a scoping review","volume":"5","author":"ER Glenn","year":"2026","journal-title":"J Orthopaed Rep"},{"key":"pone.0352200.ref024","article-title":"The growing impact of natural language processing in healthcare and public health","volume":"61","author":"A Jerfy","year":"2024","journal-title":"Inquiry"},{"key":"pone.0352200.ref025","doi-asserted-by":"crossref","first-page":"106649","DOI":"10.1016\/j.compbiomed.2023.106649","article-title":"Natural language processing in electronic health records in relation to healthcare decision-making: a systematic review","volume":"155","author":"E Hossain","year":"2023","journal-title":"Comput Biol Med"},{"key":"pone.0352200.ref026","first-page":"265","article-title":"Integrating real-time data with predictive models for early disease detection in metaverse healthcare.","author":"D Jaganathan","year":"2026","journal-title":"Federated Learning in Metaverse Healthcare. Academic Press"},{"issue":"5","key":"pone.0352200.ref027","article-title":"Unveiling the influence of AI predictive analytics on patient outcomes: a comprehensive narrative review","volume":"16","author":"D Dixon","year":"2024","journal-title":"Cureus"},{"issue":"1","key":"pone.0352200.ref028","doi-asserted-by":"crossref","first-page":"53","DOI":"10.20517\/ais.2024.45","article-title":"Has artificial intelligence in spine surgery lived up to the hype? A narrative review of recent approaches, current challenges, and the path forward","volume":"5","author":"VS Ambati","year":"2025","journal-title":"Art Int Surg"},{"issue":"1","key":"pone.0352200.ref029","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1097\/br9.0000000000000002","article-title":"Artificial intelligence in spine research: a multimodal perspective beyond imaging","volume":"1","author":"K Kita","year":"2025","journal-title":"Spine Res"},{"issue":"2","key":"pone.0352200.ref030","doi-asserted-by":"crossref","first-page":"474","DOI":"10.14245\/ns.2448388.194","article-title":"Artificial intelligence in spinal imaging and patient care: a review of recent advances","volume":"21","author":"S Lee","year":"2024","journal-title":"Neurospine"},{"issue":"12","key":"pone.0352200.ref031","doi-asserted-by":"crossref","first-page":"1435","DOI":"10.3390\/bioengineering10121435","article-title":"How artificial intelligence is shaping medical imaging technology: a survey of innovations and applications","volume":"10","author":"L Pinto-Coelho","year":"2023","journal-title":"Bioengineering (Basel)"},{"issue":"9","key":"pone.0352200.ref032","article-title":"APPRAISE-AI tool for quantitative evaluation of AI studies for clinical decision support","volume":"6","author":"JCC Kwong","year":"2023","journal-title":"JAMA Network Open"},{"key":"pone.0352200.ref033","article-title":"TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","year":"2024","journal-title":"BMJ"},{"issue":"2","key":"pone.0352200.ref034","article-title":"Automatic detection and voxel-wise mapping of lumbar spine Modic changes with deep learning","volume":"5","author":"KT Gao","year":"2022","journal-title":"JOR Spine"},{"key":"pone.0352200.ref035","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.jor.2023.03.001","article-title":"5-Year progression prediction of endplate defects: utilizing the EDPP-Flow convolutional neural network based on unbalanced data","volume":"38","author":"JPY Cheung","year":"2023","journal-title":"J Orthop"},{"issue":"4","key":"pone.0352200.ref036","doi-asserted-by":"crossref","first-page":"2298","DOI":"10.1007\/s00330-024-11080-0","article-title":"AI-based lumbar central canal stenosis classification on sagittal MR images is comparable to experienced radiologists using axial images","volume":"35","author":"JW van der Graaf","year":"2025","journal-title":"Eur Radiol"},{"key":"pone.0352200.ref037","doi-asserted-by":"crossref","first-page":"111347","DOI":"10.1016\/j.ejrad.2024.111347","article-title":"Use of MRI-based deep learning radiomics to diagnose sacroiliitis related to axial spondyloarthritis","volume":"172","author":"K Zhang","year":"2024","journal-title":"Eur J Radiol"},{"issue":"12","key":"pone.0352200.ref038","doi-asserted-by":"crossref","DOI":"10.1111\/1756-185X.70004","article-title":"The TabNet model for diagnosing axial spondyloarthritis using MRI imaging findings and clinical risk factors","volume":"27","author":"Z Zhang","year":"2024","journal-title":"Int J Rheum Dis"},{"issue":"2","key":"pone.0352200.ref039","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1148\/radiol.2020201537","article-title":"MRI-based Synthetic CT in the detection of structural lesions in patients with suspected sacroiliitis: comparison with MRI","volume":"298","author":"LBO Jans","year":"2021","journal-title":"Radiology"},{"issue":"1","key":"pone.0352200.ref040","doi-asserted-by":"crossref","first-page":"42","DOI":"10.14744\/ejmo.2023.59207","article-title":"Clinical assessment of CoLumbo Deep Learning System for Central Canal Stenosis Diagnostics","volume":"7","author":"R Georgiev","year":"2023","journal-title":"Euras J Med Oncol"},{"issue":"10","key":"pone.0352200.ref041","doi-asserted-by":"crossref","first-page":"4198","DOI":"10.1093\/rheumatology\/keac059","article-title":"Deep learning algorithms for magnetic resonance imaging of inflammatory sacroiliitis in axial spondyloarthritis","volume":"61","author":"KYY Lin","year":"2022","journal-title":"Rheumatology (Oxford)"},{"issue":"4","key":"pone.0352200.ref042","doi-asserted-by":"crossref","first-page":"1390","DOI":"10.1002\/jmri.29211","article-title":"Deep learning algorithm of the SPARCC scoring system in SI joint MRI","volume":"60","author":"Y Lin","year":"2024","journal-title":"J Magn Reson Imaging"},{"issue":"11","key":"pone.0352200.ref043","first-page":"2459","article-title":"Detection of structural lesions of the sacroiliac joints in patients with spondyloarthritis: a comparison of T1-weighted 3D spoiled gradient echo MRI and MRI-based synthetic CT versus T1-weighted turbo spin echo MRI","volume":"53","author":"S Krabbe","year":"2024","journal-title":"Skeletal Radiol"},{"issue":"17","key":"pone.0352200.ref044","doi-asserted-by":"crossref","first-page":"1870","DOI":"10.3390\/diagnostics14171870","article-title":"Validating the accuracy of a patient-facing clinical decision support system in predicting lumbar disc herniation: diagnostic accuracy study","volume":"14","author":"F Badahman","year":"2024","journal-title":"Diagnostics (Basel)"},{"issue":"3","key":"pone.0352200.ref045","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.239007","article-title":"Deep learning detects changes indicative of axial spondyloarthritis at MRI of sacroiliac joints","volume":"307","author":"KK Bressem","year":"2023","journal-title":"Radiology"},{"issue":"3","key":"pone.0352200.ref046","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1007\/s00586-024-08623-w","article-title":"Deep learning model for automated detection of fresh and old vertebral fractures on thoracolumbar CT","volume":"34","author":"J Chen","year":"2025","journal-title":"Eur Spine J"},{"issue":"3","key":"pone.0352200.ref047","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1002\/jmri.27400","article-title":"Automated grading of lumbar disc degeneration using a push-pull regularization network based on MRI","volume":"53","author":"F Gao","year":"2021","journal-title":"J Magn Reson Imaging"},{"issue":"24","key":"pone.0352200.ref048","doi-asserted-by":"crossref","first-page":"3643","DOI":"10.3390\/diagnostics13243643","article-title":"The development and validation of an AI diagnostic model for sacroiliitis: a deep-learning approach","volume":"13","author":"K-H Lee","year":"2023","journal-title":"Diagnostics (Basel)"},{"issue":"7","key":"pone.0352200.ref049","doi-asserted-by":"crossref","first-page":"1156","DOI":"10.3390\/diagnostics11071156","article-title":"Method for diagnosing the bone marrow edema of sacroiliac joint in patients with axial spondyloarthritis using magnetic resonance image analysis based on deep learning","volume":"11","author":"KH Lee","year":"2021","journal-title":"Diagnostics (Basel)"},{"issue":"18","key":"pone.0352200.ref050","doi-asserted-by":"crossref","first-page":"2975","DOI":"10.3390\/diagnostics13182975","article-title":"Nerve root compression analysis to find lumbar spine stenosis on MRI Using CNN","volume":"13","author":"T Shahzadi","year":"2023","journal-title":"Diagnostics (Basel)"},{"issue":"6","key":"pone.0352200.ref051","doi-asserted-by":"crossref","first-page":"552","DOI":"10.22603\/ssrr.2024-0154","article-title":"Artificial intelligence classification for detecting and grading lumbar intervertebral disc degeneration","volume":"8","author":"W Liawrungrueang","year":"2024","journal-title":"Spine Surg Relat Res"},{"issue":"1","key":"pone.0352200.ref052","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1148\/radiol.220076","article-title":"Improved productivity using deep learning-assisted reporting for lumbar spine MRI","volume":"305","author":"DSW Lim","year":"2022","journal-title":"Radiology"},{"issue":"9","key":"pone.0352200.ref053","doi-asserted-by":"crossref","first-page":"187","DOI":"10.3390\/jimaging9090187","article-title":"A deep learning-based model for classifying osteoporotic lumbar vertebral fractures on radiographs: a retrospective model development and validation study","volume":"9","author":"Y Ono","year":"2023","journal-title":"J Imaging"},{"key":"pone.0352200.ref054","doi-asserted-by":"crossref","first-page":"890371","DOI":"10.3389\/fendo.2022.890371","article-title":"Automatic grading of disc herniation, central canal stenosis and nerve roots compression in lumbar magnetic resonance image diagnosis","volume":"13","author":"ZH Su","year":"2022","journal-title":"Front Endocrinol"},{"issue":"12","key":"pone.0352200.ref055","doi-asserted-by":"crossref","first-page":"8656","DOI":"10.1007\/s00330-023-09918-0","article-title":"Deep learning-based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI","volume":"33","author":"H Yoo","year":"2023","journal-title":"Eur Radiol"},{"issue":"4","key":"pone.0352200.ref056","doi-asserted-by":"crossref","DOI":"10.1136\/rmdopen-2024-004628","article-title":"Anatomy-centred deep learning improves generalisability and progression prediction in radiographic sacroiliitis detection","volume":"10","author":"FJ Dorfner","year":"2024","journal-title":"RMD Open"},{"issue":"12","key":"pone.0352200.ref057","doi-asserted-by":"crossref","first-page":"2169","DOI":"10.1002\/art.42650","article-title":"Machine learning pipeline for predicting bone marrow edema along the sacroiliac joints on magnetic resonance imaging","volume":"75","author":"J Roels","year":"2023","journal-title":"Arthritis Rheumatol"},{"issue":"5","key":"pone.0352200.ref058","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.1007\/s10278-023-00858-1","article-title":"Automatic image segmentation and grading diagnosis of sacroiliitis associated with AS using a deep convolutional neural network on CT images","volume":"36","author":"K Zhang","year":"2023","journal-title":"J Digit Imaging"},{"key":"pone.0352200.ref059","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.diii.2023.03.008","article-title":"A deep learning model for the diagnosis of sacroiliitis according to Assessment of SpondyloArthritis International Society classification criteria with magnetic resonance imaging","volume":"104","author":"A Bordner","year":"2023","journal-title":"Diagn Interv Imaging"},{"issue":"10","key":"pone.0352200.ref060","doi-asserted-by":"crossref","first-page":"2187","DOI":"10.1002\/jor.24930","article-title":"Texture analysis in the classification of T2 -weighted magnetic resonance images in persons with and without low back pain","volume":"39","author":"V Abdollah","year":"2021","journal-title":"J Orthop Res"},{"issue":"1","key":"pone.0352200.ref061","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1186\/s42358-020-00126-8","article-title":"Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging","volume":"60","author":"MC Faleiros","year":"2020","journal-title":"Adv Rheumatol"},{"issue":"4","key":"pone.0352200.ref062","doi-asserted-by":"crossref","DOI":"10.1136\/rmdopen-2024-004702","article-title":"Identification of a machine learning-based diagnostic model for axial spondyloarthritis in rheumatological routine care using a random forest approach","volume":"10","author":"I Redeker","year":"2024","journal-title":"RMD Open"},{"key":"pone.0352200.ref063","doi-asserted-by":"crossref","DOI":"10.1016\/j.wneu.2024.02.101","article-title":"Will ChatGPT be able to replace a spine surgeon in the clinical setting?","volume":"185","author":"R Chalhoub","year":"2024","journal-title":"World Neurosurg"},{"issue":"3","key":"pone.0352200.ref064","doi-asserted-by":"crossref","DOI":"10.1002\/jsp2.1276","article-title":"Deep learning-based detection and classification of lumbar disc herniation on magnetic resonance images","volume":"6","author":"W Zhang","year":"2023","journal-title":"JOR Spine"},{"key":"pone.0352200.ref065","doi-asserted-by":"crossref","first-page":"1424716","DOI":"10.3389\/fsurg.2024.1424716","article-title":"MRI to digital medicine diagnosis: integrating deep learning into clinical decision-making for lumbar degenerative diseases","volume":"11","author":"B Ke","year":"2025","journal-title":"Front Surg"},{"issue":"1","key":"pone.0352200.ref066","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1111\/os.13894","article-title":"Automatic detection and classification of modic changes in MRI images using deep learning: intelligent assisted diagnosis system","volume":"16","author":"G Liu","year":"2024","journal-title":"Orthop Surg"},{"issue":"4","key":"pone.0352200.ref067","article-title":"Sacroiliitis diagnosis based on interpretable features and multi-task learning","volume":"69","author":"L Liu","year":"2024","journal-title":"Phys Med Biol"},{"key":"pone.0352200.ref068","doi-asserted-by":"crossref","first-page":"1458569","DOI":"10.3389\/fsurg.2024.1458569","article-title":"Compare three deep learning-based artificial intelligence models for classification of calcified lumbar disc herniation: a multicenter diagnostic study","volume":"11","author":"Z Liu","year":"2024","journal-title":"Front Surg"},{"issue":"1","key":"pone.0352200.ref069","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.bbe.2018.09.003","article-title":"Detection of Modic changes in MR images of spine using local binary patterns","volume":"39","author":"JS Athertya","year":"2019","journal-title":"Biocybern Biomed Eng"},{"key":"pone.0352200.ref070","article-title":"A pilot study implementing a machine learning algorithm to use artificial intelligence to diagnose spinal conditions","author":"A Soin","year":"2022","journal-title":"Pain Physician"},{"issue":"15","key":"pone.0352200.ref071","doi-asserted-by":"crossref","first-page":"2587","DOI":"10.3390\/diagnostics13152587","article-title":"Radiomics for the detection of active sacroiliitis using MR imaging","volume":"13","author":"M Triantafyllou","year":"2023","journal-title":"Diagnostics (Basel)"},{"key":"pone.0352200.ref072","first-page":"100564","article-title":"External validation of SpineNetV2 on a comprehensive set of radiological features for grading lumbosacral disc pathologies","volume":"20","author":"AS Nigru","year":"2024","journal-title":"N Am Spine Soc J"},{"issue":"5","key":"pone.0352200.ref073","doi-asserted-by":"crossref","first-page":"902","DOI":"10.3390\/diagnostics11050902","article-title":"Detection of degenerative changes on MR images of the lumbar spine with a convolutional neural network: a feasibility study","volume":"11","author":"NC Lehnen","year":"2021","journal-title":"Diagnostics (Basel)"},{"issue":"1","key":"pone.0352200.ref074","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1186\/s13075-021-02484-0","article-title":"Deep learning for detection of radiographic sacroiliitis: achieving expert-level performance","volume":"23","author":"KK Bressem","year":"2021","journal-title":"Arthritis Res Ther"},{"issue":"5","key":"pone.0352200.ref075","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0302899","article-title":"BACK-to-MOVE: Machine learning and computer vision model automating clinical classification of non-specific low back pain for personalised management","volume":"19","author":"T Hartley","year":"2024","journal-title":"PLoS One"},{"issue":"8","key":"pone.0352200.ref076","doi-asserted-by":"crossref","first-page":"1992","DOI":"10.1007\/s00586-021-07066-x","article-title":"Identification of potentially painful disc fissures in magnetic resonance images using machine-learning modelling","volume":"31","author":"K Lagerstrand","year":"2022","journal-title":"Eur Spine J"},{"issue":"23","key":"pone.0352200.ref077","doi-asserted-by":"crossref","DOI":"10.1097\/MD.0000000000033910","article-title":"Deep-learning reconstruction for the evaluation of lumbar spinal stenosis in computed tomography","volume":"102","author":"R Miyo","year":"2023","journal-title":"Medicine (Baltimore)"},{"issue":"12","key":"pone.0352200.ref078","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1007\/s00256-023-04364-x","article-title":"Image quality and lesion detectability of deep learning-accelerated T2-weighted Dixon imaging of the cervical spine","volume":"52","author":"G Seo","year":"2023","journal-title":"Skeletal Radiol"},{"issue":"1","key":"pone.0352200.ref079","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1186\/s41747-024-00470-0","article-title":"Deep learning reconstruction for lumbar spine MRI acceleration: a prospective study","volume":"8","author":"H Tang","year":"2024","journal-title":"Eur Radiol Exp"},{"issue":"7","key":"pone.0352200.ref080","doi-asserted-by":"crossref","DOI":"10.1007\/s10586-025-05281-5","article-title":"Explainable artificial intelligence for medical imaging systems using deep learning: a comprehensive review","volume":"28","author":"EH Houssein","year":"2025","journal-title":"Cluster Comput"},{"issue":"22","key":"pone.0352200.ref081","doi-asserted-by":"crossref","first-page":"8115","DOI":"10.3390\/jcm14228115","article-title":"AI in pediatric spine care: clinical, research, and ethical considerations","volume":"14","author":"HK Nugraha","year":"2025","journal-title":"J Clin Med"},{"issue":"1","key":"pone.0352200.ref082","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1177\/10944281221124947","article-title":"Advancing reproducibility and accountability of unsupervised machine learning in text mining: importance of transparency in reporting preprocessing and algorithm selection","volume":"27","author":"L Valtonen","year":"2022","journal-title":"Organ Res Methods"},{"issue":"10","key":"pone.0352200.ref083","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pdig.0000347","article-title":"Challenges and best practices for digital unstructured data enrichment in health research: a systematic narrative review","volume":"2","author":"J Sedlakova","year":"2023","journal-title":"PLOS Digit Health"},{"key":"pone.0352200.ref084","first-page":"100827","article-title":"Artificial intelligence in spine care: a scoping review of treatment applications","volume":"25","author":"VA Bensel","year":"2025","journal-title":"N Am Spine Soc J"},{"key":"pone.0352200.ref085","author":"Regulatory Compliance Associates, Inc"},{"key":"pone.0352200.ref086","year":"2024"},{"key":"pone.0352200.ref087","author":"polepole","year":"2024"},{"key":"pone.0352200.ref088","author":"The Light Collective & Digital Public","year":"2024"},{"key":"pone.0352200.ref089","author":"Coalition for Health AI","year":"2023"},{"key":"pone.0352200.ref090","volume-title":"Aiming for truth, fairness, and equity in your company\u2019s use of AI [Internet]","author":"E Jillson","year":"2021"},{"key":"pone.0352200.ref091","article-title":"A study on the framework for evaluating the ethics and trustworthiness of generative AI","author":"C Jeong","year":"2025"},{"key":"pone.0352200.ref092","doi-asserted-by":"crossref","first-page":"114789","DOI":"10.1016\/j.asoc.2026.114789","article-title":"Ethical concerns of generative AI and mitigation strategies: A systematic mapping study","volume":"193","author":"Y Huang","year":"2026","journal-title":"Appl Soft Comput"},{"key":"pone.0352200.ref093","doi-asserted-by":"crossref","DOI":"10.3389\/fhumd.2024.1421273","article-title":"Transparency and accountability in AI systems: safeguarding wellbeing in the age of algorithmic decision-making","volume":"6","author":"BC Cheong","year":"2024","journal-title":"Front Hum Dyn"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0352200","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T17:41:58Z","timestamp":1785260518000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0352200"}},"subtitle":[],"editor":[{"given":"Rajakumar","family":"Anbazhagan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,7,28]]},"references-count":93,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7,28]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0352200","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,28]]}}}