{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T07:45:56Z","timestamp":1791272756630,"version":"4.1.0"},"reference-count":44,"publisher":"Elsevier BV","issue":"2","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","theclinics.com","clinicalkey.es","clinicalkey.com.au","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging Clinics of North America"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1016\/j.mric.2025.01.001","type":"journal-article","created":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T12:21:29Z","timestamp":1739535689000},"page":"389-398","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"title":["Artificial Intelligence in Spine Imaging"],"prefix":"10.1016","volume":"33","author":[{"given":"Kushal","family":"Patel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierce","family":"Cooper","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Puneet","family":"Belani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amish","family":"Doshi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"6","key":"10.1016\/j.mric.2025.01.001_bib1","doi-asserted-by":"crossref","first-page":"e316","DOI":"10.1016\/S2665-9913(23)00098-X","article-title":"Global, regional, and national burden of low back pain, 1990\u20132020, its attributable risk factors, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021","volume":"5","author":"Ferreira","year":"2023","journal-title":"Lancet Rheumatol"},{"issue":"7","key":"10.1016\/j.mric.2025.01.001_bib2","doi-asserted-by":"crossref","first-page":"2328","DOI":"10.4103\/jfmpc.jfmpc_440_19","article-title":"Overview of artificial intelligence in medicine","volume":"8","author":"Amisha","year":"2019","journal-title":"J\u00a0Fam Med Prim Care"},{"issue":"3","key":"10.1016\/j.mric.2025.01.001_bib3","first-page":"163","article-title":"A\u00a0review of computer-aided diagnosis in thoracic and colonic imaging","volume":"2","author":"Suzuki","year":"2012","journal-title":"Quant Imaging Med Surg"},{"issue":"suppl_1","key":"10.1016\/j.mric.2025.01.001_bib4","doi-asserted-by":"crossref","first-page":"s3","DOI":"10.1259\/bjr\/82933343","article-title":"Current status and future potential of computer-aided diagnosis in medical imaging","volume":"78","author":"Doi","year":"2005","journal-title":"Br J Radiol"},{"issue":"1","key":"10.1016\/j.mric.2025.01.001_bib5","article-title":"What the radiologist should know about artificial intelligence - an ESR white paper","volume":"10","author":"Neri","year":"2019","journal-title":"Insights Imaging"},{"issue":"10","key":"10.1016\/j.mric.2025.01.001_bib6","doi-asserted-by":"crossref","first-page":"1210","DOI":"10.1016\/j.jacr.2016.04.009","article-title":"Radiology workflow disruptors: a detailed analysis","volume":"13","author":"Schemmel","year":"2016","journal-title":"J\u00a0Am Coll Radiol"},{"issue":"5","key":"10.1016\/j.mric.2025.01.001_bib7","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1093\/jamia\/ocx125","article-title":"Using machine learning for sequence-level automated MRI protocol selection in neuroradiology","volume":"25","author":"Brown","year":"2018","journal-title":"J\u00a0Am Med Inf Assoc"},{"issue":"9","key":"10.1016\/j.mric.2025.01.001_bib8","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1016\/j.jacr.2020.03.012","article-title":"Machine learning for automation of radiology protocols for quality and efficiency improvement","volume":"17","author":"Kalra","year":"2020","journal-title":"J\u00a0Am Coll Radiol"},{"issue":"7","key":"10.1016\/j.mric.2025.01.001_bib9","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/j.jacr.2015.03.007","article-title":"Toward quantifying the prevalence, severity, and cost associated with patient motion during clinical MR examinations","volume":"12","author":"Andre","year":"2015","journal-title":"J\u00a0Am Coll Radiol"},{"issue":"3","key":"10.1016\/j.mric.2025.01.001_bib10","article-title":"Impact of deep learning image reconstruction methods on MRI throughput","volume":"6","author":"Yang","year":"2024","journal-title":"RadiolArtifIntell"},{"key":"10.1016\/j.mric.2025.01.001_bib11","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejro.2024.100567","article-title":"Advanced deep learning-based image reconstruction in lumbar spine MRI at 0.55 T \u2013 Effects on image quality and acquisition time in comparison to conventional deep learning-based reconstruction","volume":"12","author":"Schlicht","year":"2024","journal-title":"Eur J Radiol Open"},{"issue":"6","key":"10.1016\/j.mric.2025.01.001_bib12","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1136\/ard-2023-225444","article-title":"MRI-based synthetic CT: a new method for structural damage assessment in the spine in patients with axial spondyloarthritis - a comparison with low-dose CT and radiography","volume":"83","author":"Willesen","year":"2024","journal-title":"Ann Rheum Dis"},{"issue":"1","key":"10.1016\/j.mric.2025.01.001_bib13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3171\/2020.10.FOCUS20801","article-title":"Magnetic resonance imaging\u2013based synthetic computed tomography of the lumbar spine for surgical planning: a clinical proof-of-concept","volume":"50","author":"Staartjes","year":"2021","journal-title":"Neurosurg Focus"},{"issue":"8","key":"10.1016\/j.mric.2025.01.001_bib14","doi-asserted-by":"crossref","first-page":"987","DOI":"10.3174\/ajnr.A7920","article-title":"Deep learning\u2013generated synthetic MR imaging STIR spine images are superior in image quality and diagnostically equivalent to conventional STIR: a multicenter, multireader trial","volume":"44","author":"Tanenbaum","year":"2023","journal-title":"Am J Neuroradiol"},{"issue":"2","key":"10.1016\/j.mric.2025.01.001_bib15","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s10278-018-0140-5","article-title":"Automatic vertebrae localization and identification by combining deep SSAE contextual features and structured regression forest","volume":"32","author":"Wang","year":"2019","journal-title":"J\u00a0Digit Imaging"},{"key":"10.1016\/j.mric.2025.01.001_bib16","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.media.2019.02.005","article-title":"Iterative fully convolutional neural networks for automatic vertebra segmentation and identification","volume":"53","author":"Lessmann","year":"2019","journal-title":"Med Image Anal"},{"key":"10.1016\/j.mric.2025.01.001_bib17","doi-asserted-by":"crossref","unstructured":"Windsor R, Jamaludin A, Kadir T, et al., A convolutional approach to vertebrae detection and labeling in whole spine MRI. Conference paper at: medical image computing and computer assisted intervention (MICCAI); October 4\u20138, 2020, Lima Peru https:\/\/doi.org\/10.1007\/978-3-030-59725-2_69.","DOI":"10.1007\/978-3-030-59725-2_69"},{"issue":"LNCS","key":"10.1016\/j.mric.2025.01.001_bib18","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/978-3-031-16437-8_26","article-title":"Context-aware transformers for spinal cancer detection and radiological grading","volume":"13433","author":"Windsor","year":"2022","journal-title":"Lect Notes Comput Sci"},{"issue":"1","key":"10.1016\/j.mric.2025.01.001_bib19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-024-64580-w","article-title":"Automated detection, labeling and radiological grading of clinical spinal MRIs","volume":"14","author":"Windsor","year":"2024","journal-title":"Sci Rep"},{"issue":"5","key":"10.1016\/j.mric.2025.01.001_bib20","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1007\/s00223-024-01196-2","article-title":"Opportunistic identification of vertebral compression fractures on CT scans of the chest and abdomen, using an AI algorithm, in a real-life setting","volume":"114","author":"Bendtsen","year":"2024","journal-title":"Calcif Tissue Int"},{"key":"10.1016\/j.mric.2025.01.001_bib21","doi-asserted-by":"crossref","DOI":"10.1093\/jbmr\/zjae096","article-title":"Vertebral compression fractures at abdominal CT: underdiagnosis, undertreatment, and evaluation of an AI algorithm","author":"Wiklund","year":"2024","journal-title":"J\u00a0Bone Miner Res"},{"issue":"4","key":"10.1016\/j.mric.2025.01.001_bib22","first-page":"535","article-title":"Validation of an artificial intelligence-based method to automate Cobb angle measurement on spinal radiographs of children with adolescent idiopathic scoliosis","volume":"54","author":"Wong","year":"2023","journal-title":"Eur J Phys Rehabil Med"},{"issue":"6","key":"10.1016\/j.mric.2025.01.001_bib23","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.210292","article-title":"Automated classification of intramedullary spinal cord tumors and inflammatory demyelinating lesions using deep learning","volume":"4","author":"Zhuo","year":"2022","journal-title":"Radiol Artif Intell"},{"issue":"18","key":"10.1016\/j.mric.2025.01.001_bib24","article-title":"Deep learning-based spinal canal segmentation of computed tomography image for disease diagnosis: a proposed system for spinal stenosis diagnosis","volume":"103","author":"Zhou","year":"2024","journal-title":"Medicine (United States)"},{"issue":"7","key":"10.1016\/j.mric.2025.01.001_bib25","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1038\/s41393-024-00993-8","article-title":"Quantification of cervical spinal stenosis by automated 3D MRI segmentation of spinal cord and cerebrospinal fluid space","volume":"62","author":"Hohenhaus","year":"2024","journal-title":"Spinal Cord"},{"key":"10.1016\/j.mric.2025.01.001_bib26","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2024.111375","article-title":"Towards clinical implementation of an AI-algorithm for detection of cervical spine fractures on computed tomography","volume":"173","author":"Ruitenbeek","year":"2024","journal-title":"Eur J Radiol"},{"issue":"4","key":"10.1016\/j.mric.2025.01.001_bib27","doi-asserted-by":"crossref","first-page":"432","DOI":"10.3174\/ajnr.A8141","article-title":"Automated detection of cervical spinal stenosis and cord compression via vision transformer and rules-based classification","volume":"45","author":"Payne","year":"2024","journal-title":"Am J Neuroradiol"},{"issue":"s3","key":"10.1016\/j.mric.2025.01.001_bib28","doi-asserted-by":"crossref","first-page":"S86","DOI":"10.14444\/7131","article-title":"Feasibility of deep learning algorithms for reporting in routine spine magnetic resonance imaging","volume":"14","author":"Lewandrowski","year":"2020","journal-title":"Int J Spine Surg"},{"key":"10.1016\/j.mric.2025.01.001_bib29","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2023.110726","article-title":"The role of Artificial intelligence in the assessment of the spine and spinal cord","volume":"161","author":"Mart\u00edn-Noguerol","year":"2023","journal-title":"Eur J Radiol"},{"key":"10.1016\/j.mric.2025.01.001_bib30","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101872","article-title":"Unifying neural learning and symbolic reasoning for spinal medical report generation","volume":"67","author":"Han","year":"2021","journal-title":"Med Image Anal"},{"issue":"2","key":"10.1016\/j.mric.2025.01.001_bib31","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1007\/s11604-023-01487-y","article-title":"Preliminary assessment of automated radiology report generation with generative pre-trained transformers: comparing results to radiologist-generated reports","volume":"42","author":"Nakaura","year":"2024","journal-title":"Jpn J Radiol"},{"issue":"5","key":"10.1016\/j.mric.2025.01.001_bib32","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.222044","article-title":"Opportunistic screening: radiology scientific expert panel","volume":"307","author":"Pickhardt","year":"2023","journal-title":"Radiology"},{"issue":"3","key":"10.1016\/j.mric.2025.01.001_bib33","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1148\/radiol.2017162100","article-title":"Vertebral body compression fractures and bone density: automated detection and classification on CT images","volume":"284","author":"Burns","year":"2017","journal-title":"Radiology"},{"issue":"3","key":"10.1016\/j.mric.2025.01.001_bib34","doi-asserted-by":"crossref","first-page":"582","DOI":"10.2214\/AJR.20.22874","article-title":"Value-added opportunistic CT: insights into osteoporosis and sarcopenia","volume":"215","author":"Boutin","year":"2020","journal-title":"Am J Roentgenol"},{"issue":"4","key":"10.1016\/j.mric.2025.01.001_bib35","doi-asserted-by":"crossref","first-page":"442","DOI":"10.3340\/jkns.2018.0178","article-title":"Machine learning model to predict osteoporotic spine with Hounsfield units on lumbar computed tomography","volume":"62","author":"Nam","year":"2019","journal-title":"J\u00a0Korean Neurosurg Soc"},{"issue":"6","key":"10.1016\/j.mric.2025.01.001_bib36","doi-asserted-by":"crossref","first-page":"3549","DOI":"10.1007\/s00330-020-06677-0","article-title":"Prediction of bone mineral density from computed tomography: application of deep learning with a convolutional neural network","volume":"30","author":"Yasaka","year":"2020","journal-title":"Eur Radiol"},{"issue":"5","key":"10.1016\/j.mric.2025.01.001_bib37","doi-asserted-by":"crossref","first-page":"3142","DOI":"10.1007\/s00330-021-08313-x","article-title":"End-to-end automated body composition analyses with integrated quality control for opportunistic assessment of sarcopenia in CT","volume":"32","author":"Nowak","year":"2022","journal-title":"Eur Radiol"},{"issue":"1","key":"10.1016\/j.mric.2025.01.001_bib38","doi-asserted-by":"crossref","first-page":"124","DOI":"10.2214\/AJR.21.26486","article-title":"Fully automated deep learning tool for sarcopenia assessment on CT: L1 versus L3 vertebral level muscle measurements for opportunistic prediction of adverse clinical outcomes","volume":"218","author":"Pickhardt","year":"2022","journal-title":"AJR Am J Roentgenol"},{"key":"10.1016\/j.mric.2025.01.001_bib39","doi-asserted-by":"crossref","DOI":"10.1093\/jamia\/ocae165","article-title":"Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging","author":"Stanley","year":"2024","journal-title":"J\u00a0Am Med Inf Assoc"},{"issue":"2","key":"10.1016\/j.mric.2025.01.001_bib40","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1007\/s41666-022-00114-1","article-title":"Explainable artificial intelligence for predictive modeling in healthcare","volume":"6","author":"Yang","year":"2022","journal-title":"J\u00a0Healthc Inform Res"},{"issue":"17","key":"10.1016\/j.mric.2025.01.001_bib41","doi-asserted-by":"crossref","DOI":"10.3390\/diagnostics13172760","article-title":"Redefining radiology: a review of artificial intelligence integration in medical imaging","volume":"13","author":"Najjar","year":"2023","journal-title":"Diagnostics"},{"key":"10.1016\/j.mric.2025.01.001_bib42","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2021.109999","article-title":"MRI-based synthetic CT of the lumbar spine: Geometric measurements for surgery planning in comparison with CT","volume":"144","author":"Morb\u00e9e","year":"2021","journal-title":"Eur J Radiol"},{"issue":"4","key":"10.1016\/j.mric.2025.01.001_bib43","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1016\/j.jacr.2023.05.006","article-title":"Validation of opportunistic artificial intelligence-based bone mineral density measurements in coronary artery calcium scans","volume":"21","author":"Naghavi","year":"2024","journal-title":"J\u00a0Am Coll Radiol"},{"issue":"1","key":"10.1016\/j.mric.2025.01.001_bib44","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s00062-021-01121-2","article-title":"Deep learning image processing enables 40% faster spinal MR scans which Match or exceed quality of standard of care : a prospective multicenter multireader study","volume":"32","author":"Bash","year":"2022","journal-title":"Clin Neuroradiol"}],"container-title":["Magnetic Resonance Imaging Clinics of North America"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1064968925000017?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1064968925000017?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T14:17:12Z","timestamp":1745590632000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1064968925000017"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5]]},"references-count":44,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["S1064968925000017"],"URL":"https:\/\/doi.org\/10.1016\/j.mric.2025.01.001","relation":{},"ISSN":["1064-9689"],"issn-type":[{"value":"1064-9689","type":"print"}],"subject":[],"published":{"date-parts":[[2025,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial Intelligence in Spine Imaging","name":"articletitle","label":"Article Title"},{"value":"Magnetic Resonance Imaging Clinics of North America","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.mric.2025.01.001","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}