Lehnen Nils Christian, Haase Robert, Faber Jennifer, Rüber Theodor, Vatter Hartmut, Radbruch Alexander, Schmeel Frederic Carsten
Department of Neuroradiology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127 Bonn, Germany.
Research Group Clinical Neuroimaging, German Center for Neurodegenerative Diseases, 53127 Bonn, Germany.
Diagnostics (Basel). 2021 May 19;11(5):902. doi: 10.3390/diagnostics11050902.
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.
我们的目标是评估基于腰椎多种磁共振成像（MR）特征训练的卷积神经网络（CNN）检测腰椎各种不同退变改变的诊断性能。146例连续患者接受了包括T2加权成像在内的腰椎常规临床MRI检查，并使用CNN对椎体、椎间盘节段以及椎间盘突出、椎间盘膨出、椎管狭窄、神经根受压和椎体滑脱的存在情况进行回顾性分析和标注。放射科医生的评估作为诊断参考标准。我们使用混淆矩阵和McNemar检验评估了CNN的诊断准确性和一致性。在我们的数据中，888个腰椎节段中共有77例椎间盘突出（其中46例进一步分类为脱出型）、133例椎间盘膨出、35例椎管狭窄、59例神经根受压和20例椎体滑脱节段。CNN在椎间盘检测和标注方面获得了完美的准确率（100%），在检测椎间盘突出（87%；95%置信区间：0.84，0.89）、脱出型（86%；95%置信区间：0.84，0.89）、膨出（76%；95%置信区间：0.73，0.78）、椎管狭窄（98%；95%置信区间：0.97，0.99）、神经根受压（91%；95%置信区间：0.89，0.92）和椎体滑脱（87.61%；95%置信区间：85.26，89.21）方面具有中度到高度的诊断准确性。我们的数据表明，使用单一综合的CNN对腰椎多种不同退变改变进行自动诊断是可行的。该CNN在椎间盘标注以及检测腰椎临床相关退变改变如椎管狭窄和椎间盘脱出方面具有较高的诊断准确性。
