Detecting ossification of the posterior longitudinal ligament on plain radiographs using a deep convolutional neural network: a pilot study

Abstract

BACKGROUND CONTEXTIts rare prevalence and subtle radiological changes often lead to difficulties in diagnosing cervical ossification of the posterior longitudinal ligament (OPLL) on plain radiographs. However, OPLL progression may lead to trauma-induced spinal cord injury, resulting in severe paralysis. To address the difficulties in diagnosis, a deep learning approach using a convolutional neural network (CNN) was applied. PURPOSEThe aim of our research was to evaluate the performance of a CNN model for diagnosing cervical OPLL. STUDY DESIGN AND SETTINGDiagnostic image study. PATIENT SAMPLEThis study included 50 patients with cervical OPLL, and 50 control patients with plain radiographs. OUTCOME MEASURESFor the CNN model performance evaluation, we calculated the area under the receiver operating characteristic curve (AUC). We also compared the sensitivity, specificity, and accuracy of the diagnosis by the CNN with those of general orthopedic surgeons and spine specialists. METHODSComputed tomography was used as the gold standard for diagnosis. Radiographs of the cervical spine in neutral, flexion, and extension positions were used for training and validation of the CNN model. We used the deep learning PyTorch framework to construct the CNN architecture. RESULTSThe accuracy of the CNN model was 90% (18/20), with a sensitivity and specificity of 80% and 100%, respectively. In contrast, the mean accuracy of orthopedic surgeons was 70%, with a sensitivity and specificity of 73% (SD: 0.12) and 67% (SD: 0.17), respectively. The mean accuracy of the spine surgeons was 75%, with a sensitivity and specificity of 80% (SD: 0.08) and 70% (SD: 0.08), respectively. The AUC of the CNN model based on the radiographs was 0.924. CONCLUSIONSThe CNN model had successful diagnostic accuracy and sufficient specificity in the diagnosis of OPLL.