Multi-Radiologist User Study for Artificial Intelligence-Guided Grading of COVID-19 Lung Disease Severity on Chest Radiographs.

Journal: Academic radiology
Published Date: Jan 18, 2021

Abstract

RATIONALE AND OBJECTIVES: Radiographic findings of COVID-19 pneumonia can be used for patient risk stratification; however, radiologist reporting of disease severity is inconsistent on chest radiographs (CXRs). We aimed to see if an artificial intelligence (AI) system could help improve radiologist interrater agreement.

Authors

  • Matthew D Li
    Department of Radiology, Harvard Medical School/Massachusetts General Hospital, Boston, Massachusets.
  • Brent P Little
    Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
  • Tarik K Alkasab
    Massachusetts General Hospital and Harvard Medical School, Radiology, 25 New Chardon Street, Suite 400B, Boston, MA, 02114, USA.
  • Dexter P Mendoza
    Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
  • Marc D Succi
    Harvard Medical School, Boston, MA, USA. msucci@mgh.harvard.edu.
  • Jo-Anne O Shepard
    Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
  • Michael H Lev
    Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
  • Jayashree Kalpathy-Cramer
    Department of Radiology, MGH/Harvard Medical School, Charlestown, Massachusetts.

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