CASIA OpenIR  > 中国科学院分子影像重点实验室
A Deep Learning Prognosis Model Help Alert for COVID-19 Patients at High-Risk of Death: A Multi-Center Study
Meng, Lingwei1,2; Dong, Di1,2; Li, Liang3; Niu, Meng4,5; Bai, Yan6,7; Wang, Meiyun6,7; Qiu, Xiaoming8; Zha, Yunfei3; Tian, Jie2,9,10
发表期刊IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
ISSN2168-2194
2020-12-01
卷号24期号:12页码:3576-3584
摘要

Since its outbreak in December 2019, the persistent coronavirus disease (COVID-19) became a global health emergency. It is imperative to develop a prognostic tool to identify high-risk patients and assist in the formulation of treatment plans. We retrospectively collected 366 severe or critical COVID-19 patients from four centers, including 70 patients who died within 14 days (labeled as high-risk patients) since their initial CT scan and 296 who survived more than 14 days or were cured (labeled as low-risk patients). We developed a 3D densely connected convolutional neural network (termed De-COVID19-Net) to predict the probability of COVID-19 patients belonging to the high-risk or low-risk group, combining CT and clinical information. The area under the curve (AUC) and other evaluation techniques were used to assess our model. The De-COVID19-Net yielded an AUC of 0.952 (95% confidence interval, 0.928-0.977) on the training set and 0.943 (0.904-0.981) on the test set. The stratified analyses indicated that our model's performance is independent of age, sex, and with/without chronic diseases. The Kaplan-Meier analysis revealed that our model could significantly categorize patients into high-risk and low-risk groups (p < 0.001). In conclusion, De-COVID19-Net can non-invasively predict whether a patient will die shortly based on the patient's initial CT scan with an impressive performance, which indicated that it could be used as a potential prognosis tool to alert high-risk patients and intervene in advance.

关键词COVID-19 Computed tomography Lung Hospitals Biomedical imaging Training Coronavirus disease 2019 (COVID-19) prognosis computed tomography deep learning artificial intelligence
DOI10.1109/JBHI.2020.3034296
关键词[WOS]WEIGHTED YOUDEN INDEX ; PREDICT
收录类别SCI
语种英语
资助项目National Key R&D Program of China[2017YFC1309100] ; National Key R&D Program of China[2017YFA0205200] ; Novel Coronavirus Pneumonia Emergency Key Project of Science and Technology of Hubei Province[2020FCA015] ; National Natural Science Foundation of China[82022036] ; National Natural Science Foundation of China[91959130] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[6202790004] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81871332] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB 38040200] ; Natural Science Foundation of Beijing Municipality[L182061] ; Project of High-Level Talents Team Introduction in Zhuhai City[Zhuhai HLHPTP201703] ; Youth Innovation Promotion Association CAS[2017175]
项目资助者National Key R&D Program of China ; Novel Coronavirus Pneumonia Emergency Key Project of Science and Technology of Hubei Province ; National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Natural Science Foundation of Beijing Municipality ; Project of High-Level Talents Team Introduction in Zhuhai City ; Youth Innovation Promotion Association CAS
WOS研究方向Computer Science ; Mathematical & Computational Biology ; Medical Informatics
WOS类目Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Medical Informatics
WOS记录号WOS:000597173000023
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类医学影像处理与分析
引用统计
被引频次:43[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/42726
专题中国科学院分子影像重点实验室
通讯作者Zha, Yunfei; Tian, Jie
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Chinese Acad Sci, CAS Key Lab Mol Imaging, Inst Automat, Beijing 100190, Peoples R China
3.Wuhan Univ, Renmin Hosp, Dept Radiol, Wuhan 430060, Peoples R China
4.China Med Univ, Hosp 1, Dept Intervent Radiol, Shenyang, Liaoning, Peoples R China
5.Harbin Med Univ, Affiliated Hosp 2, Harbin, Heilongjiang, Peoples R China
6.Zhengzhou Univ, Henan Prov Peoples Hosp, Dept Med Imaging, Zhengzhou, Henan, Peoples R China
7.Zhengzhou Univ, Peoples Hosp, Zhengzhou, Henan, Peoples R China
8.Hubei Polytech Univ, Edong Healthcare Grp, Affiliated Hosp, Dept Radiol,Huangshi Cent Hosp, Huangshi, Hubei, Peoples R China
9.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
10.Jinan Univ, Zhuhai Peoples Hosp, Zhuhai 519000, Peoples R China
推荐引用方式
GB/T 7714
Meng, Lingwei,Dong, Di,Li, Liang,et al. A Deep Learning Prognosis Model Help Alert for COVID-19 Patients at High-Risk of Death: A Multi-Center Study[J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2020,24(12):3576-3584.
APA Meng, Lingwei.,Dong, Di.,Li, Liang.,Niu, Meng.,Bai, Yan.,...&Tian, Jie.(2020).A Deep Learning Prognosis Model Help Alert for COVID-19 Patients at High-Risk of Death: A Multi-Center Study.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,24(12),3576-3584.
MLA Meng, Lingwei,et al."A Deep Learning Prognosis Model Help Alert for COVID-19 Patients at High-Risk of Death: A Multi-Center Study".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 24.12(2020):3576-3584.
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