ai-corona: Radiologist-assistant deep learning framework for COVID-19 diagnosis in chest CT scans

  1. Mehdi Yousefzadeh
  2. Parsa Esfahanian
  3. Seyed Mohammad Sadegh Movahed
  4. Saeid Gorgin
  5. Dara Rahmati
  6. Atefeh Abedini
  7. Seyed Alireza Nadji
  8. Sara Haseli
  9. Mehrdad Bakhshayesh Karam
  10. Arda Kiani
  11. Meisam Hoseinyazdi
  12. Jafar Roshandel
  13. Reza Lashgari

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Abstract

The development of medical assisting tools based on artificial intelligence advances is essential in the global fight against COVID-19 outbreak and the future of medical systems. In this study, we introduce ai-corona , a radiologist-assistant deep learning framework for COVID-19 infection diagnosis using chest CT scans. Our framework incorporates an EfficientNetB3-based feature extractor. We employed three datasets; the CC-CCII set, the MasihDaneshvari Hospital (MDH) cohort, and the MosMedData cohort. Overall, these datasets constitute 7184 scans from 5693 subjects and include the COVID-19, non-COVID abnormal (NCA), common pneumonia (CP), non-pneumonia, and Normal classes. We evaluate ai-corona on test sets from the CC-CCII set, MDH cohort, and the entirety of the MosMedData cohort, for which it gained AUC scores of 0.997, 0.989, and 0.954, respectively. Our results indicates ai-corona outperforms all the alternative models. Lastly, our framework’s diagnosis capabilities were evaluated as assistant to several experts. Accordingly, We observed an increase in both speed and accuracy of expert diagnosis when incorporating ai-corona ’s assistance.

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  1. Version published to 10.1371/journal.pone.0250952
  2. Version published to 10.1101/2020.05.04.20082081v2 on medRxiv
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    SciScore for 10.1101/2020.05.04.20082081: (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
    The vector output of the EfficientNetB3 feature extraction block is then passed through the classifier block, which contains yet another average pooling layer that is connected to the model’s output neurons corresponding to the classes via a dense network of connections. ai-corona is implemented with Python 3.7 [37] and Keras 2.3 [38] framework and was trained on NVIDIA GeForce RTX 2080 Ti for 60 epochs in a total of three hours.
    Python
    suggested: (IPython, RRID:SCR_001658)
    The Pydicom [39] package was used to read the DICOM file of the cases.
    Pydicom
    suggested: (pydicom, RRID:SCR_002573)
    All of our criteria were calculated using the scikit-learn [41] package.
    scikit-learn
    suggested: (scikit-learn, RRID:SCR_002577)

    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.
    • Thank you for including a protocol registration statement.

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  4. Version published to 10.1101/2020.05.04.20082081v1 on medRxiv