Tokyo Medical University, Tokyo, Japan.
Eur Spine J. 2021 Aug;30(8):2185-2190. doi: 10.1007/s00586-021-06914-0. Epub 2021 Jul 1.
Ossification of the posterior longitudinal ligament (OPLL) causes serious problems, such as myelopathy and acute spinal cord injury. The early and accurate diagnosis of OPLL would hence prevent the miserable prognoses. Plain lateral radiography is an essential method for the evaluation of OPLL. Therefore, minimizing the diagnostic errors of OPLL on radiography is crucial. Image identification based on a residual neural network (RNN) has been recognized to be potentially effective as a diagnostic strategy for orthopedic diseases; however, the accuracy of detecting OPLL using RNN has remained unclear. An RNN was trained with plain lateral cervical radiography images of 2,318 images from 672 patients (535 images from 304 patients with OPLL and 1,773 images from 368 patients of Negative). The accuracy, sensitivity, specificity, false positive rate, and false negative rate of diagnosis of the RNN were calculated. The mean accuracy, sensitivity, specificity, false positive rate, and false negative rate of the model were 98.9%, 97.0%, 99.4%, 2.2%, and 1.0%, respectively. The model achieved an overall area under the curve of 0.99 (95% confidence interval, 0.97-1.00) in which AUC in each fold estimated was 0.99, 0.99, 0.98, 0.98, and 0.99, respectively. An algorithm trained by an RNN could make binary classification of OPLL on cervical lateral X-ray images. RNN may hence be useful as a screening tool to assist physicians in identifying patients with OPLL in future setting. To achieve accurate identification of OPLL patients clinically, RNN has to be trained with other cause of myelopathy.
骨化的后纵韧带（OPLL）可引起严重问题，如脊髓病和急性脊髓损伤。因此，早期准确诊断 OPLL 可预防预后不良。颈椎侧位平片是评估 OPLL 的基本方法。因此，最大限度地减少平片 OPLL 的诊断错误至关重要。基于残差神经网络（RNN）的图像识别已被认为是一种有潜力的骨科疾病诊断策略；然而，使用 RNN 检测 OPLL 的准确性仍不清楚。使用来自 672 名患者的 2318 张颈椎侧位平片图像（535 张来自 304 名 OPLL 患者，1773 张来自 368 名阴性患者）对 RNN 进行训练。计算 RNN 诊断的准确性、敏感性、特异性、假阳性率和假阴性率。该模型的平均准确性、敏感性、特异性、假阳性率和假阴性率分别为 98.9%、97.0%、99.4%、2.2%和 1.0%。该模型的曲线下面积为 0.99（95%置信区间，0.97-1.00），其中每个折叠的 AUC 分别为 0.99、0.99、0.98、0.98 和 0.99。由 RNN 训练的算法可以对颈椎侧位 X 线图像进行 OPLL 的二进制分类。因此，RNN 可能成为未来识别 OPLL 患者的辅助筛查工具。为了在临床上准确识别 OPLL 患者，RNN 必须接受其他脊髓病变的训练。
