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A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis

文献类型：期刊论文

作者Wang, Shuo9; Zha, Yunfei6; Li, Weimin1; Wu, Qingxia12; Li, Xiaohu11; Niu, Meng10; Wang, Meiyun7,8; Qiu, Xiaoming5; Li, Hongjun4; Yu, He1
刊名EUROPEAN RESPIRATORY JOURNAL
出版日期2020-08-01
卷号56期号:2页码:11
ISSN号0903-1936
DOI10.1183/13993003.00775-2020
通讯作者Tian, Jie(jie.tian@ia.ac.cn)
英文摘要Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19 and finding high-risk patients with worse prognosis for early prevention and medical resource optimisation is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography. We retrospectively collected 5372 patients with computed tomography images from seven cities or provinces. Firstly, 4106 patients with computed tomography images were used to pre-train the deep learning system, making it learn lung features. Following this, 1266 patients (924 with COVID-19 (471 had follow-up for >5 days) and 342 with other pneumonia) from six cities or provinces were enrolled to train and externally validate the performance of the deep learning system. In the four external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC 0.87 and 0.88, respectively) and viral pneumonia (AUC 0.86). Moreover, the deep learning system succeeded to stratify patients into high- and low-risk groups whose hospital-stay time had significant difference (p=0.013 and p=0.014, respectively). Without human assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings. Deep learning provides a convenient tool for fast screening of COVID-19 and identifying potential high-risk patients, which may be helpful for medical resource optimisation and early prevention before patients show severe symptoms.
WOS关键词PNEUMONIA ; LUNG
资助项目National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81871332] ; National Natural Science Foundation of China[61936013] ; National Natural Science Foundation of China[81771806] ; National Key R&D Program of China[2017YFA0205200] ; Novel Coronavirus Pneumonia Emergency Key Project of Science and Technology of Hubei Province[2020FCA015] ; Fundamental Research Funds for the Central Universities[2042020kfxg10] ; Anhui Natural Science Foundation[202004a07020003] ; Hubei Health Committee General Program[WJ2019M043] ; Anti-schistosomiasis Fund during 2019-2020[WJ2019M043] ; Beijing Municipal Commission of Health[2020-TG-002] ; Youan Medical Development Fund[BJYAYY-2020YC-03] ; China Postdoctoral Science Special Foundation[2019TQ0019] ; Crossref Funder Registry
WOS研究方向Respiratory System
语种英语
WOS记录号WOS:000558225300003
出版者EUROPEAN RESPIRATORY SOC JOURNALS LTD
资助机构National Natural Science Foundation of China ; National Key R&D Program of China ; Novel Coronavirus Pneumonia Emergency Key Project of Science and Technology of Hubei Province ; Fundamental Research Funds for the Central Universities ; Anhui Natural Science Foundation ; Hubei Health Committee General Program ; Anti-schistosomiasis Fund during 2019-2020 ; Beijing Municipal Commission of Health ; Youan Medical Development Fund ; China Postdoctoral Science Special Foundation ; Crossref Funder Registry
源URL[http://ir.ia.ac.cn/handle/173211/40442]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Tian, Jie
作者单位1.Sichuan Univ, West China Hosp, Dept Resp & Crit Care Med, Chengdu, Sichuan, Peoples R China
2.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Minist Educ, Sch Life Sci & Technol, Xian, Peoples R China
3.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
4.Capital Med Univ, Dept Radiol, Beijing Youan Hosp, Beijing, Peoples R China
5.Hubei Polytech Univ, Huangshi Cent Hosp, Dept Radiol, Edong Healthcare Grp,Affiliated Hosp, Huangshi, Hubei, Peoples R China
6.Wuhan Univ, Dept Radiol, Renmin Hosp, Wuhan, Peoples R China
7.Zhengzhou Univ, Peoples Hosp, Zhengzhou, Peoples R China
8.Zhengzhou Univ, Henan Prov Peoples Hosp, Dept Med Imaging, Zhengzhou, Peoples R China
9.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med & Engn, Beijing, Peoples R China
10.China Med Univ, Dept Intervent Radiol, Hosp 1, Shenyang, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Shuo,Zha, Yunfei,Li, Weimin,et al. A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis[J]. EUROPEAN RESPIRATORY JOURNAL,2020,56(2):11.
APA Wang, Shuo.,Zha, Yunfei.,Li, Weimin.,Wu, Qingxia.,Li, Xiaohu.,...&Tian, Jie.(2020).A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis.EUROPEAN RESPIRATORY JOURNAL,56(2),11.
MLA Wang, Shuo,et al."A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis".EUROPEAN RESPIRATORY JOURNAL 56.2(2020):11.

入库方式： OAI收割

来源：自动化研究所

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