Assisting scalable diagnosis automatically via CT images in the combat against COVID-19

  1. Bohan Liu
  2. Pan Liu
  3. Lutao Dai
  4. Yanlin Yang
  5. Peng Xie
  6. Yiqing Tan
  7. Jicheng Du
  8. Wei Shan
  9. Chenghui Zhao
  10. Qin Zhong
  11. Xixiang Lin
  12. Xizhou Guan
  13. Ning Xing
  14. Yuhui Sun
  15. Wenjun Wang
  16. Zhibing Zhang
  17. Xia Fu
  18. Yanqing Fan
  19. Meifang Li
  20. Na Zhang
  21. Lin Li
  22. Yaou Liu
  23. Lin Xu
  24. Jingbo Du
  25. Zhenhua Zhao
  26. Xuelong Hu
  27. Weipeng Fan
  28. Rongpin Wang
  29. Chongchong Wu
  30. Yongkang Nie
  31. Liuquan Cheng
  32. Lin Ma
  33. Zongren Li
  34. Qian Jia
  35. Minchao Liu
  36. Huayuan Guo
  37. Gao Huang
  38. Haipeng Shen
  39. Liang Zhang
  40. Peifang Zhang
  41. Gang Guo
  42. Hao Li
  43. Weimin An
  44. Jianxin Zhou
  45. Kunlun He

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

The pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.

Article activity feed

  1. Version published to 10.1038/s41598-021-83424-5
  2. ScreenIT

    SciScore for 10.1101/2020.05.11.20093732: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The model was developed using TensorFlow (version 1.8 with CUDA V9.1.85 and cuDNN 7.0.5) on 16 Tesla P100 GPU.
    TensorFlow
    suggested: (tensorflow, RRID:SCR_016345)
    Evaluation results were obtained and visualized using python libraries, including NumPy, pandas, scikit-learn and Matplotlib.
    python
    suggested: (IPython, RRID:SCR_001658)
    NumPy
    suggested: (NumPy, RRID:SCR_008633)
    scikit-learn
    suggested: (scikit-learn, RRID:SCR_002577)
    Matplotlib
    suggested: (MatPlotLib, RRID:SCR_008624)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.05.11.20093732v1 on medRxiv