g-CXR-Net: A Graphic Application for the Rapid Recognition of SARS-CoV-2 from Chest X-Rays

  1. Domenico L. Gatti

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Abstract

g-CXR-Net is a graphic application for the rapid recognition of SARS-CoV-2 from Antero/Posterior chest X-rays. It employs the Artificial Intelligence engine of CXR-Net ( arXiv:2103.00087 ) to generate masks of the lungs overlapping the heart and large vasa, probabilities for Covid vs . non-Covid assignment, and high resolution heat maps that identify the SARS associated lung regions.

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    SciScore for 10.1101/2021.06.06.21258428: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code.

    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.

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2021.06.06.21258428v1 on medRxiv