Toward understanding COVID-19 pneumonia: a deep-learning-based approach for severity analysis and monitoring the disease

  1. Mohammadreza Zandehshahvar
  2. Marly van Assen
  3. Hossein Maleki
  4. Yashar Kiarashi
  5. Carlo N. De Cecco
  6. Ali Adibi

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Abstract

We report a new approach using artificial intelligence (AI) to study and classify the severity of COVID-19 using 1208 chest X-rays (CXRs) of 396 COVID-19 patients obtained through the course of the disease at Emory Healthcare affiliated hospitals (Atlanta, GA, USA). Using a two-stage transfer learning technique to train a convolutional neural network (CNN), we show that the algorithm is able to classify four classes of disease severity (normal, mild, moderate, and severe) with the average Area Under the Curve (AUC) of 0.93. In addition, we show that the outputs of different layers of the CNN under dominant filters provide valuable insight about the subtle patterns in the CXRs, which can improve the accuracy in the reading of CXRs by a radiologist. Finally, we show that our approach can be used for studying the disease progression in a single patient and its influencing factors. The results suggest that our technique can form the foundation of a more concrete clinical model to predict the evolution of COVID-19 severity and the efficacy of different treatments for each patient through using CXRs and clinical data in the early stages of the disease. This use of AI to assess the severity and possibly predicting the future stages of the disease early on, will be essential in dealing with the upcoming waves of COVID-19 and optimizing resource allocation and treatment.

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  1. Version published to 10.1038/s41598-021-90411-3
  2. ScreenIT

    SciScore for 10.1101/2020.11.24.20235887: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: The CXRs are collected from January 1st, 2020 to May 1st, 2020, and the need for informed consent is waived by the institutional review board (IRB).
    RandomizationAll the images are blinded and randomized for the qualification of disease severity, and the reader had no insight into clinical data and/or outcomes.
    BlindingAll the images are blinded and randomized for the qualification of disease severity, and the reader had no insight into clinical data and/or outcomes.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    4.1 COVID-19 dataset population and labeling procedure: Patients included retrospectively for this study are selected as a consecutive sample who had a clinically performed positive COVID-19 RT-PCR test related to their CXR imaging date at Emory Healthcare affiliated hospitals (Atlanta, GA, USA).
    Emory Healthcare
    suggested: (One Mind Biospecimen Bank Listing, RRID:SCR_004193)
    Algorithms are implemented in Python using the Keras package and trained on a workstation with an NVIDIA RTX2080 GPU, a Core i7 CPU, and 32 GB of RAM.
    Python
    suggested: (IPython, RRID:SCR_001658)

    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 10.1101/2020.11.24.20235887v1 on medRxiv