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Article

Detection of Degenerative Changes on MR Images of the Lumbar Spine with a Convolutional Neural Network: A Feasibility Study

1
Department of Neuroradiology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127 Bonn, Germany
2
Research Group Clinical Neuroimaging, German Center for Neurodegenerative Diseases, 53127 Bonn, Germany
3
Department of Neurology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127 Bonn, Germany
4
Department of Epileptology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127 Bonn, Germany
5
Department of Neurosurgery, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127 Bonn, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Thomas Baum and Nico Sollmann
Diagnostics 2021, 11(5), 902; https://doi.org/10.3390/diagnostics11050902
Received: 28 April 2021 / Revised: 16 May 2021 / Accepted: 17 May 2021 / Published: 19 May 2021
(This article belongs to the Special Issue Spine Imaging: Novel Image Acquisition Techniques and Analysis Tools)
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. View Full-Text
Keywords: deep learning; lumbar spine; MRI; automated reading; diagnostic performance; disc protrusion; disc bulging; spinal canal stenosis; nerve root compression; spondylolisthesis deep learning; lumbar spine; MRI; automated reading; diagnostic performance; disc protrusion; disc bulging; spinal canal stenosis; nerve root compression; spondylolisthesis
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MDPI and ACS Style

Lehnen, N.C.; Haase, R.; Faber, J.; Rüber, T.; Vatter, H.; Radbruch, A.; Schmeel, F.C. Detection of Degenerative Changes on MR Images of the Lumbar Spine with a Convolutional Neural Network: A Feasibility Study. Diagnostics 2021, 11, 902. https://doi.org/10.3390/diagnostics11050902

AMA Style

Lehnen NC, Haase R, Faber J, Rüber T, Vatter H, Radbruch A, Schmeel FC. Detection of Degenerative Changes on MR Images of the Lumbar Spine with a Convolutional Neural Network: A Feasibility Study. Diagnostics. 2021; 11(5):902. https://doi.org/10.3390/diagnostics11050902

Chicago/Turabian Style

Lehnen, Nils C., Robert Haase, Jennifer Faber, Theodor Rüber, Hartmut Vatter, Alexander Radbruch, and Frederic C. Schmeel. 2021. "Detection of Degenerative Changes on MR Images of the Lumbar Spine with a Convolutional Neural Network: A Feasibility Study" Diagnostics 11, no. 5: 902. https://doi.org/10.3390/diagnostics11050902

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