Using artificial intelligence to risk stratify COVID19 patients based on chest X-ray findings



Deep learning-based radiological image analysis could facilitate use of chest x-rays as a triaging tool for COVID-19 diagnosis in resource-limited settings. This study sought to determine whether a modified commercially available deep learning algorithm (M-qXR) could risk stratify patients with suspected COVID-19 infections. 


A dual track clinical validation study was designed to assess the clinical accuracy of M-qXR. The algorithm evaluated all Chest-X-rays (CXRs) performed during the study period for abnormal findings and assigned a COVID-19 risk score. Four independent radiologists served as radiological ground truth. The M-qXR algorithm output was compared against radiological ground truth and summary statistics for prediction accuracy were calculated. In addition, patients who underwent both PCR testing and CXR for suspected COVID-19 infection were included in a co-occurrence matrix to assess the sensitivity and specificity of the M-qXR algorithm. 


625 CXRs were included in the clinical validation study. 98% of total interpretations made by M-qXR agreed with ground truth (p = 0.25). M-qXR correctly identified the presence or absence of pulmonary opacities in 94% of CXR interpretations. M-qXR’s sensitivity, specificity, PPV, and NPV for detecting pulmonary opacities were 94%, 95%, 99%, and 88% respectively. M-qXR correctly identified the presence or absence of pulmonary consolidation in 88% of CXR interpretations (p = 0.48). M-qXR’s sensitivity, specificity, PPV, and NPV for detecting pulmonary consolidation were 91%, 84%, 89%, and 86% respectively. Furthermore, 113 PCR-confirmed COVID-19 cases were used to create a co-occurrence matrix between M-qXR’s COVID-19 risk score and COVID-19 PCR test results. The PPV and NPV of a medium to high COVID-19 risk score assigned by M-qXR yielding a positive COVID-19 PCR test result was estimated to be 89.7% and 80.4% respectively. 


M-qXR was found to have comparable accuracy to radiological ground truth in detecting radiographic abnormalities on CXR suggestive of COVID-19. 


  • Diego A Hipolito Canario1, Eric Fromke1, Matthew A. Patetta1, Mohamed T. Eltilib1, Juan P. Reyes-Gonzalez2, Georgina Cornelio Rodriguez2, Valeria A. Fusco Cornejo3, Seymour Dunckner3 and Jessica K. Stewart4. 1


  • 1. UNC School of Medicine
  • 2. University of North Carolina at Chapel Hill
  • 3. Bondurant Hall
  • 4. CB 9535
  • 5. Chapel Hill
  • 6. NC
  • 7. 27599-3280
  • 8. United States Department of Radiology
  • 9. Angeles del Pedregal Hospital
  • 10. Camino de Sta
  • 11. Teresa 1055-S
  • 12. Héroes de Padierna
  • 13. La Magdalena Contreras
  • 14. 10700
  • 15. Ciudad de México
  • 16. Mexico Mindscale
  • 17. 800 W El Camino Real Suite 180 Mountain View
  • 18. CA
  • 19. 94040
  • 20. United States Division of Interventional Radiology
  • 21. Department of Radiology
  • 22. David Geffen School of Medicine
  • 23. University of California at Los Angeles. 757 Westwood Plaza
  • 24. Suite 2125
  • 25. Los Angeles
  • 26. CA
  • 27. 90095
  • 28. United States

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