使用侧位颈椎 X 光片评估颈椎病性脊髓病的深度学习算法。
Deep learning algorithm to evaluate cervical spondylotic myelopathy using lateral cervical spine radiograph.
机构信息
Department of Orthopedic Surgery, Yeungnam University College of Medicine, Yeungnam University, Medical Center, 170 Hyonchung-ro, Namgu, Daegu, 42415, South Korea.
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, 38541, Republic of Korea.
出版信息
BMC Neurol. 2022 Apr 20;22(1):147. doi: 10.1186/s12883-022-02670-w.
BACKGROUND
Deep learning (DL) is an advanced machine learning approach used in different areas such as image analysis, bioinformatics, and natural language processing. A convolutional neural network (CNN) is a representative DL model that is highly advantageous for imaging recognition and classification This study aimed to develop a CNN using lateral cervical spine radiograph to detect cervical spondylotic myelopathy (CSM).
METHODS
We retrospectively recruited 207 patients who visited the spine center of a university hospital. Of them, 96 had CSM (CSM patients) while 111 did not have CSM (non-CSM patients). CNN algorithm was used to detect cervical spondylotic myelopathy. Of the included patients, 70% (145 images) were assigned randomly to the training set, while the remaining 30% (62 images) to the test set to measure the model performance.
RESULTS
The accuracy of detecting CSM was 87.1%, and the area under the curve was 0.864 (95% CI, 0.780-0.949).
CONCLUSION
The CNN model using the lateral cervical spine radiographs of each patient could be helpful in the diagnosis of CSM.
背景
深度学习（DL）是一种应用于图像分析、生物信息学和自然语言处理等不同领域的先进机器学习方法。卷积神经网络（CNN）是一种代表性的 DL 模型，非常有利于成像识别和分类。本研究旨在开发一种使用侧位颈椎 X 光片来检测颈椎病性脊髓病（CSM）的 CNN。
方法
我们回顾性招募了 207 名就诊于大学医院脊柱中心的患者。其中 96 例患有 CSM（CSM 患者），111 例无 CSM（非 CSM 患者）。使用 CNN 算法检测颈椎病性脊髓病。纳入的患者中，70%（145 张图像）被随机分配到训练集，其余 30%（62 张图像）到测试集，以测量模型性能。
结果
检测 CSM 的准确率为 87.1%，曲线下面积为 0.864（95%CI，0.780-0.949）。
结论
使用每位患者的侧位颈椎 X 光片的 CNN 模型有助于 CSM 的诊断。
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