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@ARTICLE{Lehnen:157775,
      author       = {Lehnen, Nils Christian and Haase, Robert and Faber,
                      Jennifer and Rüber, Theodor and Vatter, Hartmut and
                      Radbruch, Alexander and Schmeel, Frederic Carsten},
      title        = {{D}etection of {D}egenerative {C}hanges on {MR} {I}mages of
                      the {L}umbar {S}pine with a {C}onvolutional {N}eural
                      {N}etwork: {A} {F}easibility {S}tudy.},
      journal      = {Diagnostics},
      volume       = {11},
      number       = {5},
      issn         = {2075-4418},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {DZNE-2021-01232},
      pages        = {902},
      year         = {2021},
      abstract     = {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's diagnostic
                      accuracy and consistency using confusion matrices and
                      McNemar's 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.},
      keywords     = {MRI (Other) / automated reading (Other) / deep learning
                      (Other) / diagnostic performance (Other) / disc bulging
                      (Other) / disc protrusion (Other) / lumbar spine (Other) /
                      nerve root compression (Other) / spinal canal stenosis
                      (Other) / spondylolisthesis (Other)},
      cin          = {AG Radbruch},
      ddc          = {610},
      cid          = {I:(DE-2719)5000075},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:34069362},
      pmc          = {pmc:PMC8158737},
      doi          = {10.3390/diagnostics11050902},
      url          = {https://pub.dzne.de/record/157775},
}