Using Artificial Intelligence for COVID-19 Chest X-ray Diagnosis

  1. Andrew Borkowski

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Abstract

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  1. Version published to 10.12788/fp.0045
  2. ScreenIT

    SciScore for 10.1101/2020.05.21.20106518: (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
    17 To balance the dataset, we expanded the COVID-19 dataset to 500 images by slight rotation (probability=1, max rotation=5) and zooming (probability=0.5, percentage_area=0.9) of the original images using the Augmentor python package.
    Augmentor
    suggested: None
    python
    suggested: (IPython, RRID:SCR_001658)
    An Angular web app was created with TensorFlow.js.
    TensorFlow
    suggested: (tensorflow, RRID:SCR_016345)

    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: We detected the following sentences addressing limitations in the study:
    We focused our study on the potential of AI in the examination of CXRs in patients with COVID-19, as there are several limitations to the routine use of CT scans with conditions such as COVID-19. Aside from the more considerable time required to obtain CTs, there are issues with contamination of CT suites, sometimes requiring a dedicated COVID-19 CT scanner.24,29 The time constraints of decontamination or limited utilization of CT suites can delay or disrupt services for both COVID-19 and non-COVID-19 patients. Because of these factors, CXR may be a better resource to minimize the risk of infection to other patients. Besides, accurate assessment of abnormalities on CXR for COVID-19 may identify patients in whom the CXR was performed for other purposes.24 CXR is more readily available than CT, especially in more remote or underdeveloped areas.29 Finally, as with CT, CXR abnormalities are reported to have appeared before RT-PCR tests became positive in a minority of patients.24 CXR findings described in COVID-19 patients are similar to those of CT and include GGOs, consolidation, and hazy increased opacities.24,26,27,29,30 Like CT, the majority of patients demonstrated greater involvement in the lower zones and peripherally24,26,27,29,30 Most patients showed bilateral involvement. However, while these findings are common in COVID-19 patients, they are not specific and can be seen in other conditions such as other viral pneumonia, bacterial pneumonia, injury from drug toxicity, ...

    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

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  3. Version published to 10.1101/2020.05.21.20106518 on medRxiv