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Deep Covid Detect: An International Experience on Covid-19 Lung Detection and Prognosis Using Chest Ct Publisher



Lee EH1 ; Zheng J1 ; Colak E2 ; Mohammadzadeh M3 ; Houshmand G4 ; Bevins N5 ; Kitamura F6 ; Altinmakas E7 ; Reis EP8 ; Kim JK9 ; Klochko C4 ; Han M1 ; Moradian S10 ; Mohammadzadeh A4 Show All Authors
Authors
  1. Lee EH1
  2. Zheng J1
  3. Colak E2
  4. Mohammadzadeh M3
  5. Houshmand G4
  6. Bevins N5
  7. Kitamura F6
  8. Altinmakas E7
  9. Reis EP8
  10. Kim JK9
  11. Klochko C4
  12. Han M1
  13. Moradian S10
  14. Mohammadzadeh A4
  15. Sharifian H3
  16. Hashemi H11
  17. Firouznia K11
  18. Ghanaati H11
  19. Gity M11
  20. Dogan H7
  21. Salehinejad H2
  22. Alves H6
  23. Seekins J1
  24. Abdala N6
  25. Atasoy C7
  26. Pouraliakbar H4
  27. Maleki M4
  28. Wong SS12
  29. Yeom KW1
Show Affiliations
Authors Affiliations
  1. 1. Department of Radiology, School of Medicine, Stanford University, Stanford, 94305, CA, United States
  2. 2. Unity Health Toronto, University of Toronto, Toronto, M5S, ON, Canada
  3. 3. Division of Radiology, Amir Alam Hospital, Tehran University of Medical Sciences, Tehran, Iran
  4. 4. Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
  5. 5. Henry Ford Health System, Detroit, MI, United States
  6. 6. Universidade Federal de Sao Paulo (UNIFESP), Sao Paulo, Brazil
  7. 7. Department of Radiology, Koc University School of Medicine, Istanbul, Turkey
  8. 8. Hospital Israelita Albert Einstein, Sao Paulo, Brazil
  9. 9. Department of Radiology, School of Medicine, Kyungpook National University, Daegu, South Korea
  10. 10. School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
  11. 11. Advanced Diagnostic and Interventional Radiology Research Center(ADIR), Medical Imaging Center, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
  12. 12. Department of Electrical Engineering, Stanford University, Stanford, 94305, CA, United States

Source: npj Digital Medicine Published:2021


Abstract

The Coronavirus disease 2019 (COVID-19) presents open questions in how we clinically diagnose and assess disease course. Recently, chest computed tomography (CT) has shown utility for COVID-19 diagnosis. In this study, we developed Deep COVID DeteCT (DCD), a deep learning convolutional neural network (CNN) that uses the entire chest CT volume to automatically predict COVID-19 (COVID+) from non-COVID-19 (COVID−) pneumonia and normal controls. We discuss training strategies and differences in performance across 13 international institutions and 8 countries. The inclusion of non-China sites in training significantly improved classification performance with area under the curve (AUCs) and accuracies above 0.8 on most test sites. Furthermore, using available follow-up scans, we investigate methods to track patient disease course and predict prognosis. © 2021, The Author(s).
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