Deep learning-based triage and analysis of lesion burden for COVID-19: a retrospective study with external validation.

Journal: The Lancet. Digital health
Published Date:

Abstract

BACKGROUND: Prompt identification of patients suspected to have COVID-19 is crucial for disease control. We aimed to develop a deep learning algorithm on the basis of chest CT for rapid triaging in fever clinics.

Authors

  • Minghuan Wang
    Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Chen Xia
    Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China.
  • Lu Huang
    School of Food Science and Technology, Dalian Polytechnic University, National Engineering Research Center of Seafood, Dalian 116034, PR China.
  • Shabei Xu
    Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Chuan Qin
    Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Jun Liu
    Department of Radiology, Second Xiangya Hospital, Changsha, Hunan, China.
  • Ying Cao
  • Pengxin Yu
    Institute of Advanced Research, Infervision, Beijing, China.
  • Tingting Zhu
    Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, OX3 7DQ, UK.
  • Hui Zhu
  • Chaonan Wu
    Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Rongguo Zhang
    Infervision, Beijing, China.
  • Xiangyu Chen
  • Jianming Wang
    School of Business Administration, Zhejiang University of Finance & Economics, Hangzhou 310018, China. sjwjm@zufe.edu.cn.
  • Guang Du
    Xianning Centre Hospital, Huanggang, China.
  • Chen Zhang
    Department of Dermatology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
  • Shaokang Wang
    Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China.
  • Kuan Chen
    Infervision, Beijing, China.
  • Zheng Liu
    ICSC World Laboratory, Geneva, Switzerland.
  • Liming Xia
    From the Department of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, the Netherlands (Q.T., E.H.M.P., D.P.S., A.d.R., H.J.L., R.J.v.d.G.); Department of Electrical Engineering, Fudan University, Shanghai, China (W.Y., Y.W.); Multidisciplinary Cardiovascular Research Centre & Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, England (P.G., S.P.); Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (L.H., L.X.); and Departments of Cardiology (M.S.) and Radiology (J.T.), Institute for Clinical and Experimental Medicine, Prague, Czech Republic.
  • Wei Wang
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.