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AI detection of mild COVID-19 pneumonia from chest CT scans

文献类型：期刊论文

作者Yao, Jin-Cao3,4; Wang, Tao5; Hou, Guang-Hua6; Ou, Di3,4; Li, Wei3,4; Zhu, Qiao-Dan3,4; Chen, Wen-Cong2; Yang, Chen3,4; Wang, Li-Jing3,4; Wang, Li-Ping3,4
刊名EUROPEAN RADIOLOGY
出版日期2021-03-18
关键词Computer-assisted diagnosis Volume CT COVID-19 Artificial intelligence Deep learning
ISSN号0938-7994
DOI10.1007/s00330-021-07797-x
通讯作者Xu, Dong(xudong@zjcc.org.cn) ; Li, Ya-Qing(yaqing_li@163.com)
英文摘要Objectives An artificial intelligence model was adopted to identify mild COVID-19 pneumonia from computed tomography (CT) volumes, and its diagnostic performance was then evaluated. Methods In this retrospective multicenter study, an atrous convolution-based deep learning model was established for the computer-assisted diagnosis of mild COVID-19 pneumonia. The dataset included 2087 chest CT exams collected from four hospitals between 1 January 2019 and 31 May 2020. The true positive rate, true negative rate, receiver operating characteristic curve, area under the curve (AUC) and convolutional feature map were used to evaluate the model. Results The proposed deep learning model was trained on 1538 patients and tested on an independent testing cohort of 549 patients. The overall sensitivity was 91.5% (195/213; p < 0.001, 95% CI: 89.2-93.9%), the overall specificity was 90.5% (304/336; p < 0.001, 95% CI: 88.0-92.9%) and the general AUC value was 0.955 (p < 0.001). Conclusions A deep learning model can accurately detect COVID-19 and serve as an important supplement to the COVID-19 reverse transcription-polymerase chain reaction (RT-PCR) test.
资助项目Zhejiang Provincial Natural Science Foundation of China[LZY21F030001] ; National Natural Science Foundation of China[81870028] ; National Natural Science Foundation of China[81871370] ; Zhejiang provincial program for the Cultivation of High-level Innovative Health Talents[A-2017-CXCR02]
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000630283200008
出版者SPRINGER
资助机构Zhejiang Provincial Natural Science Foundation of China ; National Natural Science Foundation of China ; Zhejiang provincial program for the Cultivation of High-level Innovative Health Talents
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/121440]  
专题中国科学院合肥物质科学研究院
通讯作者Xu, Dong; Li, Ya-Qing
作者单位1.Zhejiang Prov Peoples Hosp, Dept Resp Med, Hangzhou, Peoples R China
2.Vanderbilt Univ, Med Ctr, Dept Biostat, Nashville, TN USA
3.Univ Chinese Acad Sci, Canc Hosp, Zhejiang Canc Hosp, 1 East Banshan Rd, Hangzhou, Zhejiang, Peoples R China
4.Chinese Acad Sci, Inst Basic Med & Canc IBMC, Hangzhou, Peoples R China
5.Huazhong Univ Sci & Technol, Tongji Hosp, Dept Resp & Crit Care Med, Tongji Med Coll, Wuhan, Peoples R China
6.Jianghan Univ, Huangpi Peoples Hosp, Dept Infect Med, Wuhan, Peoples R China
推荐引用方式
GB/T 7714		 Yao, Jin-Cao,Wang, Tao,Hou, Guang-Hua,et al. AI detection of mild COVID-19 pneumonia from chest CT scans[J]. EUROPEAN RADIOLOGY,2021.
APA Yao, Jin-Cao.,Wang, Tao.,Hou, Guang-Hua.,Ou, Di.,Li, Wei.,...&Li, Ya-Qing.(2021).AI detection of mild COVID-19 pneumonia from chest CT scans.EUROPEAN RADIOLOGY.
MLA Yao, Jin-Cao,et al."AI detection of mild COVID-19 pneumonia from chest CT scans".EUROPEAN RADIOLOGY (2021).

入库方式： OAI收割

来源：合肥物质科学研究院

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