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

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 Long, Nicole Varble, Stephanie M. Walker, Ulas Bagci, Anna Maria Ierardi, Elvira Stellato, Guido Giovanni Plensich, Giuseppe Franceschelli, Cristiano Girlando, Giovanni Irmici, Dominic Labella, Dima Hammoud, Ashkan Malayeri, Elizabeth Jones, Ronald M. Summers, Peter L. Choyke, Daguang Xu, Mona Flores, Kaku Tamura, Hirofumi Obinata, Hitoshi Mori, Francesca Patella, Maurizio Cariati, Gianpaolo Carrafiello, Peng An, Bradford J. Wood () and Baris Turkbey ()
Additional contact information
Stephanie A. Harmon: National Institutes of Health
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
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
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

Nature Communications, 2020, vol. 11, issue 1, 1-7

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-17971-2

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