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Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy 期刊论文 WOS高被引论文 WOS热点论文

编号：

CAF69CD28A5158D2C809EE9117B4105F
作者：

Li, Lin#^[1,2]Qin, Lixin#^[3]Xu, Zeguo^[1];Yin, Youbing^[4];Wang, Xin^[4];Kong, Bin^[4];Bai, Junjie^[4];Lu, Yi^[4];Fang, Zhenghan^[4];Song, Qi(宋麒)^[4]Cao, Kunlin(曹坤琳)^[4]Liu, Daliang(刘大亮)^[5]Wang, Guisheng(王贵生)^[6]Xu, Qizhong^[7];Fang, Xisheng^[1];Zhang, Shiqin^[1];Xia, Juan*^[1]Xia, Jun(夏军)*^[7]
语种：

英文
期刊：

RADIOLOGY ISSN：0033-8419 2020 年 296 卷 2 期 (E65 - +) ; AUG
收录：
摘要：

Background: Coronavirus disease 2019 (COVID-19) has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT.
Purpose: To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performance.
Materials and Methods: In this retrospective and multicenter study, a deep learning model, the COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT scans for the detection of COVID-19. CT scans of community-acquired pneumonia (CAP) and other non-pneumonia abnormalities were included to test the robustness of the model. The datasets were collected from six hospitals between August 2016 and February 2020. Diagnostic performance was assessed with the area under the receiver operating characteristic curve, sensitivity, and specificity.
Results: The collected dataset consisted of 4352 chest CT scans from 3322 patients. The average patient age (+/- standard deviation) was 49 years +/- 15, and there were slightly more men than women (1838 vs 1484, respectively; P =.29). The per-scan sensitivity and specificity for detecting COVID-19 in the independent test set was 90% (95% confidence interval [CI]: 83%, 94%; 114 of 127 scans) and 96% (95% CI: 93%, 98%; 294 of 307 scans), respectively, with an area under the receiver operating characteristic curve of 0.96 (P<.001). The per-scan sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175 scans) and 92% (239 of 259 scans), respectively, with an area under the receiver operating characteristic curve of 0.95 (95% CI: 0.93, 0.97).
Conclusion: A deep learning model can accurately detect coronavirus 2019 and differentiate it from community-acquired pneumonia and other lung conditions. (C) RSNA, 2020
推荐引用方式
GB/T 7714：

Li Lin,Qin Lixin,Xu Zeguo, et al. Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy [J].RADIOLOGY,2020,296(2):E65-+.
APA：

Li Lin,Qin Lixin,Xu Zeguo,Yin Youbing,&Xia Jun.(2020).Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy .RADIOLOGY,296(2):E65-+.
MLA：

Li Lin, et al. "Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy" .RADIOLOGY 296,2(2020):E65-+.
入库时间：

2/23/2021 9:15:05 AM
更新时间：

2/23/2021 9:15:05 AM
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