Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images.
Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images.
复制标题
深度学习利用 CT 图像准确诊断新型冠状病毒 (COVID-19)
DOI：
10.1109/tcbb.2021.3065361
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发表时间：
2021-11
期刊：
影响因子：
--
通讯作者：
Yang Y
中科院分区：
文献类型：
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作者：
Song Y;Zheng S;Li L;Zhang X;Zhang X;Huang Z;Chen J;Wang R;Zhao H;Chong Y;Shen J;Zha Y;Yang Y
新型冠状病毒（COVID-19）最近以急性呼吸道综合征的形式出现，并在全球范围内引发了肺炎疫情。随着COVID-19继续在全球迅速传播，计算机断层扫描（CT）对于快速诊断至关重要。因此，迫切需要开发一种准确的计算机辅助方法，以帮助临床医生通过CT图像识别COVID-19感染的患者。在这里，我们收集了来自中国两个省医院的88名确诊为COVID-19的患者，100名细菌性肺炎患者和86名健康人的胸部CT扫描，以进行比较和建模。基于这些数据，开发了一个基于深度学习的CT诊断系统，以识别COVID-19患者。实验结果表明，我们的模型可以准确区分COVID-19患者和细菌性肺炎患者，AUC为0.95，召回率（灵敏度）为0.96，精确度为0.79。当整合三种类型的CT图像时，我们的模型在区分COVID-19患者与其他患者方面实现了0.93的召回率和0.86的准确率。此外，我们的模型可以提取主要的病变特征，特别是毛玻璃样阴影（GGO），这是视觉上有助于医生的辅助诊断。我们的服务器（http：//biomed.nscc-gz.cn/model.php）提供在线服务器，用于在线诊断CT图像。源代码和数据集可在我们的GitHub（https：//github.com/SY575/COVID19-CT）上获得。
A novel coronavirus (COVID-19) recently emerged as an acute respiratory syndrome, and has caused a pneumonia outbreak world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. Here, we have collected chest CT scans of 88 patients diagnosed with COVID-19 from hospitals of two provinces in China, 100 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the data, a deep learning-based CT diagnosis system was developed to identify patients with COVID-19. The experimental results showed that our model could accurately discriminate the COVID-19 patients from the bacteria pneumonia patients with an AUC of 0.95, recall (sensitivity) of 0.96, and precision of 0.79. When integrating three types of CT images, our model achieved a recall of 0.93 with precision of 0.86 for discriminating COVID-19 patients from others. Moreover, our model could extract main lesion features, especially the ground-glass opacity (GGO), which are visually helpful for assisted diagnoses by doctors. An online server is available for online diagnoses with CT images by our server (http://biomed.nscc-gz.cn/model.php). Source codes and datasets are available at our GitHub (https://github.com/SY575/COVID19-CT).