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

Journal: The European respiratory journal
PMID: 32444412

Abstract

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.

Authors

  • Shuo Wang
    College of Tea & Food Science, Anhui Agricultural University, Hefei, China.
  • Yunfei Zha
    Department of Radiology, Department of Infection Prevention and Control, Renmin Hospital, Wuhan University, Wuhan, China.
  • Weimin Li
    Department of Psychiatry, Sleep Medicine Center, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Qingxia Wu
    College of Medicine and Biomedical Information Engineering, Northeastern University, Shenyang, Liaoning, China.
  • Xiaohu Li
    Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022, Hefei, Anhui, China.
  • Meng Niu
    Intervention Radiology Department, The First Hospital of China Medical University, Shenyang, 110001, China. Electronic address: niumeng@cmu.edu.cn.
  • Meiyun Wang
  • Xiaoming Qiu
    Dept of Radiology, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, Edong Healthcare Group, Huangshi, China.
  • Hongjun Li
    School of Agricultural Engineering and Food Science, Shandong University of Technology, Zhangdian District, No. 12, Zhangzhou Road, Zibo, Shandong Province, China.
  • He Yu
    Dept of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.
  • Wei Gong
    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei 430079, China.
  • Yan Bai
    Department of Radiology, Henan Provincial People's Hospital, China.
  • Li Li
    Department of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
  • Yongbei Zhu
  • Liusu Wang
    Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China.
  • Jie Tian
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.

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