@Article{info:doi/10.2196/14755，作者=“Pan, Qiong and Zhang, Kai and He, Lin and Dong, Zhou and Zhang, Lei and Wu, Xiaohang and Wu, Yi and Gao, Yanjun”，标题=“基于深度卷积神经网络的腰椎磁共振图像自动诊断椎间盘突出:方法发展研究”，期刊=“JMIR Med Inform”，年=“2021”，月=“5”，日=“21”，卷=“9”，数=“5”，页=“e14755”，关键词=“深度学习”;对象定位;磁盘形成疝;磁盘隆起;，摘要=“背景:椎间盘突出和椎间盘突出是腰椎间盘(IVDs)常见的两种疾病，常导致麻木、下肢疼痛和下背痛。磁共振成像是诊断腰椎疾病最有效的技术之一，已广泛应用于医院的临床诊断。然而，缺乏有效的工具来解释大量的MR图像，以满足许多放射科医生的要求。目的:本研究的目的是提出一个自动诊断椎间盘突出和突出的系统，可以节省时间，有效地减少放射科医生的工作量。方法:腰椎疾患的诊断高度依赖医学影像。因此，我们选择了椎间盘突出和椎间盘突出这两种最常见的疾病作为研究对象。 This study is mainly about identifying the position of IVDs (lumbar vertebra [L] 1 to L2, L2-L3, L3-L4, L4-L5, and L5 to sacral vertebra [S] 1) by analyzing the geometrical relationship between sagittal and axial images and classifying axial lumbar disk MR images via deep convolutional neural networks. Results: This system involved 4 steps. In the first step, it automatically located vertebral bodies (including the L1, L2, L3, L4, L5, and S1) in sagittal images by using the faster region-based convolutional neural network, and our fourfold cross-validation showed 100{\%} accuracy. In the second step, it spontaneously identified the corresponding disk in each axial lumbar disk MR image with 100{\%} accuracy. In the third step, the accuracy for automatically locating the intervertebral disk region of interest in axial MR images was 100{\%}. In the fourth step, the 3-class classification (normal disk, disk bulge, and disk herniation) accuracies for the L1-L2, L2-L3, L3-L4, L4-L5, and L5-S1 IVDs were 92.7{\%}, 84.4{\%}, 92.1{\%}, 90.4{\%}, and 84.2{\%}, respectively. Conclusions: The automatic diagnosis system was successfully built, and it could classify images of normal disks, disk bulge, and disk herniation. This system provided a web-based test for interpreting lumbar disk MR images that could significantly improve diagnostic efficiency and standardized diagnosis reports. This system can also be used to detect other lumbar abnormalities and cervical spondylosis. ", issn="2291-9694", doi="10.2196/14755", url="https://medinform.www.mybigtv.com/2021/5/e14755", url="https://doi.org/10.2196/14755", url="http://www.ncbi.nlm.nih.gov/pubmed/34018488" }