A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)
A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)
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DOI：
10.1080/1064119x.2021.1966557
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发表时间：
2021-02-24
影响因子：
5.9
通讯作者：
Xu, Bo
中科院分区：
文献类型：
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作者：
Wang, Shuai;Kang, Bo;Xu, Bo
目的严重急性呼吸道综合征冠状病毒2型（SARS-COV-2）暴发以来，全球已累计确诊冠状病毒病（COVID-19）2600多万例。为了控制疾病的传播，筛选大量疑似病例以进行适当的隔离和治疗是一个优先事项。病原性实验室检测通常是金标准，但它承担了重大假阴性的负担，增加了对替代诊断方法的迫切需要，以对抗这种疾病。基于CT图像中的COVID-19放射学变化，该研究假设人工智能方法可能能够提取COVID-19的特定图形特征，并在病原学测试之前提供临床诊断，从而为疾病控制节省关键时间。方法收集1065例经病原学确诊的COVID-19患者的CT图像，其中沿着既往诊断为典型病毒性肺炎的患者。我们修改了初始迁移学习模型来建立算法，然后进行了内部和外部验证。结果内部验证的总准确率为89.5%，特异性为0.88，敏感性为0.87。外部测试数据集显示总准确度为79.3%，特异性为0.83，灵敏度为0.67。此外，在54张COVID-19图像中，前两次核酸检测结果均为阴性，算法预测46张为COVID-19阳性，准确率为85.2%。结论这些结果证明了使用人工智能提取放射学特征以及时准确诊断COVID-19的原理证明。
Objective The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 26 million cases of Corona virus disease (COVID-19) in the world so far. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment are a priority. Pathogenic laboratory testing is typically the gold standard, but it bears the burden of significant false negativity, adding to the urgent need of alternative diagnostic methods to combat the disease. Based on COVID-19 radiographic changes in CT images, this study hypothesized that artificial intelligence methods might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. Methods We collected 1065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the inception transfer-learning model to establish the algorithm, followed by internal and external validation. Results The internal validation achieved a total accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with a specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images, the first two nucleic acid test results were negative, and 46 were predicted as COVID-19 positive by the algorithm, with an accuracy of 85.2%. Conclusion These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis.