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Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets

Author

Listed:
  • Stephanie A. Harmon

    (National Institutes of Health
    Frederick National Laboratory for Cancer Research)

  • Thomas H. Sanford

    (State University of New York-Upstate Medical Center)

  • Sheng Xu

    (National Institutes of Health)

  • Evrim B. Turkbey

    (National Institutes of Health)

  • Holger Roth

    (NVIDIA Corporation)

  • Ziyue Xu

    (NVIDIA Corporation)

  • Dong Yang

    (NVIDIA Corporation)

  • Andriy Myronenko

    (NVIDIA Corporation)

  • Victoria Anderson

    (National Institutes of Health)

  • Amel Amalou

    (National Institutes of Health)

  • Maxime Blain

    (National Institutes of Health)

  • Michael Kassin

    (National Institutes of Health)

  • Dilara Long

    (National Institutes of Health)

  • Nicole Varble

    (National Institutes of Health
    Philips Research North America)

  • Stephanie M. Walker

    (National Institutes of Health)

  • Ulas Bagci

    (University of Central Florida)

  • Anna Maria Ierardi

    (Ospedale Maggiore Policlinico Milano)

  • Elvira Stellato

    (Ospedale Maggiore Policlinico Milano)

  • Guido Giovanni Plensich

    (Ospedale Maggiore Policlinico Milano)

  • Giuseppe Franceschelli

    (San Paolo Hospital)

  • Cristiano Girlando

    (Università Degli Studi di Milano)

  • Giovanni Irmici

    (Università Degli Studi di Milano)

  • Dominic Labella

    (State University of New York-Upstate Medical Center)

  • Dima Hammoud

    (National Institutes of Health)

  • Ashkan Malayeri

    (National Institutes of Health)

  • Elizabeth Jones

    (National Institutes of Health)

  • Ronald M. Summers

    (National Institutes of Health)

  • Peter L. Choyke

    (National Institutes of Health)

  • Daguang Xu

    (NVIDIA Corporation)

  • Mona Flores

    (NVIDIA Corporation)

  • Kaku Tamura

    (Self-Defense Forces Central Hospital)

  • Hirofumi Obinata

    (Self-Defense Forces Central Hospital)

  • Hitoshi Mori

    (Self-Defense Forces Central Hospital)

  • Francesca Patella

    (San Paolo Hospital)

  • Maurizio Cariati

    (San Paolo Hospital)

  • Gianpaolo Carrafiello

    (Ospedale Maggiore Policlinico Milano
    University of Milano)

  • Peng An

    (Xiangyang NO.1 People’s Hospital Affiliated to Hubei University of Medicine Xiangyang)

  • Bradford J. Wood

    (National Institutes of Health)

  • Baris Turkbey

    (National Institutes of Health)

Abstract

Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.

Suggested Citation

  • Stephanie A. Harmon & Thomas H. Sanford & Sheng Xu & Evrim B. Turkbey & Holger Roth & Ziyue Xu & Dong Yang & Andriy Myronenko & Victoria Anderson & Amel Amalou & Maxime Blain & Michael Kassin & Dilara, 2020. "Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets," Nature Communications, Nature, vol. 11(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17971-2
    DOI: 10.1038/s41467-020-17971-2
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    Cited by:

    1. Winter, Jenifer Sunrise & Davidson, Elizabeth, 2022. "Harmonizing regulatory regimes for the governance of patient-generated health data," Telecommunications Policy, Elsevier, vol. 46(5).
    2. Aggarwal, Sakshi, 2023. "Machine Learning algorithms, perspectives, and real-world application: Empirical evidence from United States trade data," MPRA Paper 116579, University Library of Munich, Germany.

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