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：
10.1007/s00330-021-07715-1
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发表时间：
2021-08
影响因子：
5.9
通讯作者：
Xu B
中科院分区：
文献类型：
--
作者：
Wang S;Kang B;Ma J;Zeng X;Xiao M;Guo J;Cai M;Yang J;Li Y;Meng X;Xu B
严重急性呼吸综合征冠状病毒2型的暴发，迄今已导致全球2600多万人感染冠状病毒病(新冠肺炎)。为了控制疾病的传播，优先对大量疑似病例进行适当的检疫和治疗。病原学实验室检测通常是金标准，但它承担着显著的假阴性的负担，增加了对抗疾病的替代诊断方法的迫切需要。基于新冠肺炎在CT图像中的放射变化，本研究假设人工智能方法可能能够提取新冠肺炎的特定图形特征，并在病原学检测之前提供临床诊断，从而节省疾病控制的关键时间。我们收集了1,065例经病原体确认的新冠肺炎病例以及以前诊断为典型病毒性肺炎的病例的CT图像。我们对初始迁移学习模型进行了修正，建立了算法，并进行了内部和外部验证。内部验证的总准确率为89.5%，特异度为0.88，敏感度为0.87。外部测试数据集的总准确率为79.3%，特异度为0.83，敏感度为0.67。此外，在54张新冠肺炎图像中，前2张核酸检测结果为阴性，46张算法预测为新冠肺炎阳性，准确率为85.2%。这些结果证明了利用人工智能提取放射特征进行及时准确的新冠肺炎诊断的原理。·该研究评估了在流感季节使用CT图像筛查新冠肺炎的深度学习算法的诊断性能。·作为一种筛选方法，我们的模型在国内外CT图像数据集上取得了较高的灵敏度。·该模型用于区分新冠肺炎和其他典型的病毒性肺炎，两者的放射学特征非常相似。
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. 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. 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%. These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. • The study evaluated the diagnostic performance of a deep learning algorithm using CT images to screen for COVID-19 during the influenza season. • As a screening method, our model achieved a relatively high sensitivity on internal and external CT image datasets. • The model was used to distinguish between COVID-19 and other typical viral pneumonia, both of which have quite similar radiologic characteristics.
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DOI：
10.1089/omi.2018.0097
发表时间：
2018-10
期刊：
Omics : a journal of integrative biology
影响因子：
--
作者：
Grapov D;Fahrmann J;Wanichthanarak K;Khoomrung S
通讯作者：
Khoomrung S
影响因子：
168.9
作者：
Huang, Chaolin;Wang, Yeming;Cao, Bin
通讯作者：
Cao, Bin
中国武汉市 138 例 2019 新型冠状病毒感染肺炎住院患者的临床特征。
影响因子：
120.7
作者：
Wang, Dawei;Hu, Bo;Peng, Zhiyong
通讯作者：
Peng, Zhiyong
影响因子：
1.2
作者：
Martinez-Camblor, Pablo;Carlos Pardo-Fernandez, Juan
通讯作者：
Carlos Pardo-Fernandez, Juan
影响因子：
2.4
作者：
Shen, Lin;Li, Qi;Xu, Ruihua
通讯作者：
Xu, Ruihua