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000157775 1001_ $$0P:(DE-2719)9001552$$aLehnen, Nils Christian$$b0$$eFirst author
000157775 245__ $$aDetection of Degenerative Changes on MR Images of the Lumbar Spine with a Convolutional Neural Network: A Feasibility Study.
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000157775 520__ $$aOur 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.
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000157775 650_7 $$2Other$$aMRI
000157775 650_7 $$2Other$$aautomated reading
000157775 650_7 $$2Other$$adeep learning
000157775 650_7 $$2Other$$adiagnostic performance
000157775 650_7 $$2Other$$adisc bulging
000157775 650_7 $$2Other$$adisc protrusion
000157775 650_7 $$2Other$$alumbar spine
000157775 650_7 $$2Other$$anerve root compression
000157775 650_7 $$2Other$$aspinal canal stenosis
000157775 650_7 $$2Other$$aspondylolisthesis
000157775 7001_ $$0P:(DE-2719)9001860$$aHaase, Robert$$b1
000157775 7001_ $$0P:(DE-2719)2811327$$aFaber, Jennifer$$b2
000157775 7001_ $$00000-0002-6180-7671$$aRüber, Theodor$$b3
000157775 7001_ $$aVatter, Hartmut$$b4
000157775 7001_ $$0P:(DE-2719)9001861$$aRadbruch, Alexander$$b5$$udzne
000157775 7001_ $$0P:(DE-2719)9001551$$aSchmeel, Frederic Carsten$$b6$$eLast author
000157775 770__ $$aSpine Imaging: Novel Image Acquisition Techniques and Analysis Tools
000157775 773__ $$0PERI:(DE-600)2662336-5$$a10.3390/diagnostics11050902$$gVol. 11, no. 5, p. 902 -$$n5$$p902$$tDiagnostics$$v11$$x2075-4418$$y2021
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