The usage of deep neural network improves distinguishing COVID-19 from other suspected viral pneumonia by clinicians on chest CT: a real-world study.

Journal: European radiology
Published Date: Dec 28, 2020

Abstract

OBJECTIVES: Based on the current clinical routine, we aimed to develop a novel deep learning model to distinguish coronavirus disease 2019 (COVID-19) pneumonia from other types of pneumonia and validate it with a real-world dataset (RWD).

Authors

  • Qiuchen Xie
    Department of Radiology, Huashan Hospital, Fudan University, 12 Middle Wulumuqi Rd., Jing'an District, Shanghai, 200040, China.
  • Yiping Lu
    Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, Shanghai, 200040, China.
  • Xiancheng Xie
    Shanghai Yidan Information Technology Co., Ltd; Shanghai Key Laboratory of Data Science, Shanghai Institute for Advanced Communication and Data Science, School of Computer Science, Fudan University, Shanghai, China.
  • Nan Mei
    Department of Radiology, Huashan Hospital, Fudan University, 12 Middle Wulumuqi Rd., Jing'an District, Shanghai, 200040, China.
  • Yun Xiong
    Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China.
  • Xuanxuan Li
    Department of Radiology, Huashan Hospital Affiliated to Fudan University, 12 Wulumuqi Rd. Middle, Shanghai 200040, China.
  • Yangyong Zhu
    Shanghai Key Laboratory of Data Science, Shanghai Institute for Advanced Communication and Data Science, School of Computer Science, Fudan University, Shanghai, China.
  • Anling Xiao
    Department of Radiology, Fuyang No. 2 People's Hospital, 450 Linquan Road, Fuyang, Anhui Province, China.
  • Bo Yin
    College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, 730000 People's Republic of China.

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