Machine learning and AI aided tool to differentiate COVID 19 and non-COVID 19 lung CXR

  1. Andrio Adwibowo

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Abstract

One of the main challenges in dealing with the current COVID 19 pandemic is how to detect and distinguish between the COVID 19 and non COVID 19 cases. This problem arises since COVID 19 symptoms resemble with other cases. One of the golden standards is by examining the lung using the chest X ray radiograph (CXR). Currently there is growing COVID 19 cases followed by the CXR images waiting to be analyzed and this may outnumber the health capacity. Learning from that current situation and to fulfill the demand for CXRs analysis, a novel solution is required. The tool is expected can detect and distinguish the COVID 19 case lung rely on CXR. Respectively, this study aims to propose the use of AI and machine learning aided tool to distinguish the COVID 19 and non COVID 19 cases based on the CXR lung image. The compared non COVID 19 CXR cases in this study include normal (healthy), influenza A, tuberculosis, and active smoker. The results confirm that the machine learning tool is able to distinguish the COVID 19 CXR lungs based on lung consolidation. Moreover, the tool is also able to recognize an abnormality of COVID 19 lung in the form of patchy ground glass opacity.

To conclude, AI and machine learning may be considered as a detection tool to identify and distinguish between COVID 19 and non COVID 19 cases in particular epidemic areas.

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    SciScore for 10.1101/2020.08.18.20175521: (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

    No key resources detected.

    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.

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  2. Version published to 10.1101/2020.08.18.20175521v1 on medRxiv