{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T18:46:27Z","timestamp":1793385987924,"version":"4.5.0"},"reference-count":47,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,5,19]],"date-time":"2021-05-19T00:00:00Z","timestamp":1621382400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Our objective was to evaluate the diagnostic performance of a convolutional neural network (CNN) trained on multiple MR imaging features of the lumbar spine, to detect a variety of different degenerative changes of the lumbar spine. One hundred and forty-six consecutive patients underwent routine clinical MRI of the lumbar spine including T2-weighted imaging and were retrospectively analyzed using a CNN for detection and labeling of vertebrae, disc segments, as well as presence of disc herniation, disc bulging, spinal canal stenosis, nerve root compression, and spondylolisthesis. The assessment of a radiologist served as the diagnostic reference standard. We assessed the CNN\u2019s diagnostic accuracy and consistency using confusion matrices and McNemar\u2019s test. In our data, 77 disc herniations (thereof 46 further classified as extrusions), 133 disc bulgings, 35 spinal canal stenoses, 59 nerve root compressions, and 20 segments with spondylolisthesis were present in a total of 888 lumbar spine segments. The CNN yielded a perfect accuracy score for intervertebral disc detection and labeling (100%), and moderate to high diagnostic accuracy for the detection of disc herniations (87%; 95% CI: 0.84, 0.89), extrusions (86%; 95% CI: 0.84, 0.89), bulgings (76%; 95% CI: 0.73, 0.78), spinal canal stenoses (98%; 95% CI: 0.97, 0.99), nerve root compressions (91%; 95% CI: 0.89, 0.92), and spondylolisthesis (87.61%; 95% CI: 85.26, 89.21), respectively. Our data suggest that automatic diagnosis of multiple different degenerative changes of the lumbar spine is feasible using a single comprehensive CNN. The CNN provides high diagnostic accuracy for intervertebral disc labeling and detection of clinically relevant degenerative changes such as spinal canal stenosis and disc extrusion of the lumbar spine.<\/jats:p>","DOI":"10.3390\/diagnostics11050902","type":"journal-article","created":{"date-parts":[[2021,5,19]],"date-time":"2021-05-19T12:58:07Z","timestamp":1621429087000},"page":"902","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["Detection of Degenerative Changes on MR Images of the Lumbar Spine with a Convolutional Neural Network: A Feasibility Study"],"prefix":"10.3390","volume":"11","author":[{"given":"Nils Christian","family":"Lehnen","sequence":"first","affiliation":[{"name":"Department of Neuroradiology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universit\u00e4t Bonn, 53127 Bonn, Germany"},{"name":"Research Group Clinical Neuroimaging, German Center for Neurodegenerative Diseases, 53127 Bonn, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Haase","sequence":"additional","affiliation":[{"name":"Department of Neuroradiology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universit\u00e4t Bonn, 53127 Bonn, Germany"},{"name":"Research Group Clinical Neuroimaging, German Center for Neurodegenerative Diseases, 53127 Bonn, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer","family":"Faber","sequence":"additional","affiliation":[{"name":"Department of Neurology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universit\u00e4t Bonn, 53127 Bonn, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6180-7671","authenticated-orcid":false,"given":"Theodor","family":"R\u00fcber","sequence":"additional","affiliation":[{"name":"Department of Epileptology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universit\u00e4t Bonn, 53127 Bonn, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hartmut","family":"Vatter","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universit\u00e4t Bonn, 53127 Bonn, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Radbruch","sequence":"additional","affiliation":[{"name":"Department of Neuroradiology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universit\u00e4t Bonn, 53127 Bonn, Germany"},{"name":"Research Group Clinical Neuroimaging, German Center for Neurodegenerative Diseases, 53127 Bonn, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0985-1517","authenticated-orcid":false,"given":"Frederic Carsten","family":"Schmeel","sequence":"additional","affiliation":[{"name":"Department of Neuroradiology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universit\u00e4t Bonn, 53127 Bonn, Germany"},{"name":"Research Group Clinical Neuroimaging, German Center for Neurodegenerative Diseases, 53127 Bonn, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1016\/S0140-6736(20)30925-9","article-title":"Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u20132019: A systematic analysis for the Global Burden of Disease Study 2019","volume":"396","author":"Vos","year":"2020","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1016\/j.jacr.2016.06.008","article-title":"ACR appropriateness criteria low back pain","volume":"13","author":"Patel","year":"2016","journal-title":"J. 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