Department of Orthopaedic and Spine Surgery, Tokyo Medical and Dental University Graduate School of Medicine, Tokyo, Japan.
Department of Orthopaedic and Spine Surgery, Tokyo Medical and Dental University Graduate School of Medicine, Tokyo, Japan.
Spine J. 2022 Jun;22(6):934-940. doi: 10.1016/j.spinee.2022.01.004. Epub 2022 Jan 10.
Its 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.
The aim of our research was to evaluate the performance of a CNN model for diagnosing cervical OPLL.
Diagnostic image study.
This study included 50 patients with cervical OPLL, and 50 control patients with plain radiographs.
For 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.
Computed 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.
The 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.
The CNN model had successful diagnostic accuracy and sufficient specificity in the diagnosis of OPLL.
颈椎后纵韧带骨化症（OPLL）的罕见患病率和细微的影像学改变常常导致在普通 X 光片上难以诊断。然而，OPLL 的进展可能导致创伤性脊髓损伤，从而导致严重瘫痪。为了解决诊断困难的问题，应用了一种基于卷积神经网络（CNN）的深度学习方法。
本研究旨在评估 CNN 模型在诊断颈椎 OPLL 中的性能。
诊断性影像学研究。
本研究纳入了 50 例颈椎 OPLL 患者和 50 例普通 X 光片的对照患者。
对于 CNN 模型的性能评估，我们计算了接收者操作特征曲线下的面积（AUC）。我们还比较了 CNN 诊断的敏感性、特异性和准确性与普通骨科医生和脊柱专家的诊断结果。
采用计算机断层扫描作为诊断的金标准。使用颈椎中立位、前屈位和后伸位的 X 光片对 CNN 模型进行训练和验证。我们使用深度学习 PyTorch 框架构建了 CNN 架构。
CNN 模型的准确率为 90%（18/20），敏感性和特异性分别为 80%和 100%。相比之下，骨科医生的平均准确率为 70%，敏感性和特异性分别为 73%（标准差：0.12）和 67%（标准差：0.17）。脊柱外科医生的平均准确率为 75%，敏感性和特异性分别为 80%（标准差：0.08）和 70%（标准差：0.08）。基于 X 光片的 CNN 模型的 AUC 为 0.924。
CNN 模型在 OPLL 的诊断中具有较高的准确率和足够的特异性。
