Artificial intelligence–enabled rapid diagnosis of patients with COVID-19

  1. Xueyan Mei
  2. Hao-Chih Lee
  3. Kai-yue Diao
  4. Mingqian Huang
  5. Bin Lin
  6. Chenyu Liu
  7. Zongyu Xie
  8. Yixuan Ma
  9. Philip M. Robson
  10. Michael Chung
  11. Adam Bernheim
  12. Venkatesh Mani
  13. Claudia Calcagno
  14. Kunwei Li
  15. Shaolin Li
  16. Hong Shan
  17. Jian Lv
  18. Tongtong Zhao
  19. Junli Xia
  20. Qihua Long
  21. Sharon Steinberger
  22. Adam Jacobi
  23. Timothy Deyer
  24. Marta Luksza
  25. Fang Liu
  26. Brent P. Little
  27. Zahi A. Fayad
  28. Yang Yang

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  1. ScreenIT

    SciScore for 10.1101/2020.04.12.20062661: (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
    We used the LabelEncoder function in scikit-learn package to encode the target categorical variables into numerical variables.
    scikit-learn
    suggested: (scikit-learn, RRID:SCR_002577)
    McNemar’s test and the exact confidence intervals were calculated in the statsmodels package in python.
    python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code and data.

    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.

  2. Version published to 10.1038/s41591-020-0931-3
  3. Version published to 10.1101/2020.04.12.20062661v2 on medRxiv
  4. Version published to 10.1101/2020.04.12.20062661v1 on medRxiv