{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T02:19:18Z","timestamp":1785291558753,"version":"3.55.0"},"reference-count":40,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2022,6,8]],"date-time":"2022-06-08T00:00:00Z","timestamp":1654646400000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000069","name":"National Institute of Arthritis and Musculoskeletal and Skin Diseases","doi-asserted-by":"publisher","award":["UH2AR076724"],"award-info":[{"award-number":["UH2AR076724"]}],"id":[{"id":"10.13039\/100000069","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003653","name":"Korea National Institute of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003653","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["JOR Spine"],"published-print":{"date-parts":[[2022,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Modic changes (MCs) are the most prevalent classification system for describing magnetic resonance imaging (MRI) signal intensity changes in the vertebrae. However, there is a growing need for novel quantitative and standardized methods of characterizing these anomalies, particularly for lesions of transitional or mixed nature, due to the lack of conclusive evidence of their associations with low back pain. This retrospective imaging study aims to develop an interpretable deep learning\u2010based detection tool for voxel\u2010wise mapping of MCs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Seventy\u2010five lumbar spine MRI exams that presented with acute\u2010to\u2010chronic low back pain, radiculopathy, and other symptoms of the lumbar spine were enrolled. The pipeline consists of two deep convolutional neural networks to generate an interpretable voxel\u2010wise <jats:italic>Modic map<\/jats:italic>. First, an autoencoder was trained to segment vertebral bodies from T<jats:sub>1<\/jats:sub>\u2010weighted sagittal lumbar spine images. Next, two radiologists segmented and labeled MCs from a combined T<jats:sub>1<\/jats:sub>\u2010 and T<jats:sub>2<\/jats:sub>\u2010weighted assessment to serve as ground truth for training a second autoencoder that performs segmentation of MCs. The voxels in the detected regions were then categorized to the appropriate Modic type using a rule\u2010based signal intensity algorithm. Post hoc, three radiologists independently graded a second dataset with the aid of the model predictions in an artificial (AI)\u2010assisted experiment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The model successfully identified the presence of changes in 85.7% of samples in the unseen test set with a sensitivity of 0.71 (\u00b10.072), specificity of 0.95 (\u00b10.022), and Cohen's kappa score of 0.63. In the AI\u2010assisted experiment, the agreement between the junior radiologist and the senior neuroradiologist significantly improved from Cohen's kappa score of 0.52 to 0.58 (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>This deep learning\u2010based approach demonstrates substantial agreement with radiologists and may serve as a tool to improve inter\u2010rater reliability in the assessment of MCs.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jsp2.1204","type":"journal-article","created":{"date-parts":[[2022,6,9]],"date-time":"2022-06-09T06:39:37Z","timestamp":1654756777000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Automatic detection and voxel\u2010wise mapping of lumbar spine Modic changes with deep learning"],"prefix":"10.1002","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5975-0127","authenticated-orcid":false,"given":"Kenneth T.","family":"Gao","sequence":"first","affiliation":[{"name":"Department of Radiology and Biomedical Imaging University of California, San Francisco  San Francisco California USA"},{"name":"Department of Bioengineering University of California Berkeley\u2013University of California San Francisco Graduate Program in Bioengineering  Berkeley California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Radhika","family":"Tibrewala","sequence":"additional","affiliation":[{"name":"Department of Radiology and Biomedical Imaging University of California, San Francisco  San Francisco California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Madeline","family":"Hess","sequence":"additional","affiliation":[{"name":"Department of Radiology and Biomedical Imaging University of California, San Francisco  San Francisco California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Upasana U.","family":"Bharadwaj","sequence":"additional","affiliation":[{"name":"Department of Radiology and Biomedical Imaging University of California, San Francisco  San Francisco California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaurav","family":"Inamdar","sequence":"additional","affiliation":[{"name":"Department of Radiology and Biomedical Imaging University of California, San Francisco  San Francisco California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas M.","family":"Link","sequence":"additional","affiliation":[{"name":"Department of Radiology and Biomedical Imaging University of California, San Francisco  San Francisco California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cynthia T.","family":"Chin","sequence":"additional","affiliation":[{"name":"Department of Radiology and Biomedical Imaging University of California, San Francisco  San Francisco California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valentina","family":"Pedoia","sequence":"additional","affiliation":[{"name":"Department of Radiology and Biomedical Imaging University of California, San Francisco  San Francisco California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sharmila","family":"Majumdar","sequence":"additional","affiliation":[{"name":"Department of Radiology and Biomedical Imaging University of California, San Francisco  San Francisco California 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