医学
金标准(测试)
骨科手术
接收机工作特性
射线照相术
卷积神经网络
后纵韧带骨化
深度学习
放射科
核医学
脊髓病
外科
人工智能
脊髓
内科学
精神科
计算机科学
作者
Takahisa Ogawa,Toshitaka Yoshii,Jun Oyama,Nobuhiro Sugimura,Takashi Akada,Takaaki Sugino,Motonori Hashimoto,Shingo Morishita,Takuya Takahashi,Takayuki Motoyoshi,Takuya Oyaizu,Tsuyoshi Yamada,Hiroaki Onuma,Takashi Hirai,Hiroyuki Inose,Yoshikazu Nakajima,Atsushi Okawa
标识
DOI:10.1016/j.spinee.2022.01.004
摘要
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.
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