Automated Detection and Quantification of COVID-19 Airspace Disease on Chest Radiographs: A Novel Approach Achieving Expert Radiologist-Level Performance Using a Deep Convolutional Neural Network Trained on Digital Reconstructed Radiographs From Computed Tomography-Derived Ground Truth.

Journal: Investigative radiology
Published Date: Aug 1, 2021

Abstract

OBJECTIVES: The aim of this study was to leverage volumetric quantification of airspace disease (AD) derived from a superior modality (computed tomography [CT]) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to (1) train a convolutional neural network (CNN) to quantify AD on paired chest radiographs (CXRs) and CTs, and (2) compare the DRR-trained CNN to expert human readers in the CXR evaluation of patients with confirmed COVID-19.

Authors

  • Eduardo J Mortani Barbosa
    Department of Bioengineering, School of Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania. Electronic address: Eduardo.Barbosa@pennmedicine.upenn.edu.
  • Warren B Gefter
    Department of Radiology, University of Pennsylvania Perelman School of Medicine.
  • Florin C Ghesu
  • Siqi Liu
    National University of Singapore Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore.
  • Boris Mailhe
  • Awais Mansoor
    Center for Infectious Disease Imaging (CIDI), Radiology and Imaging Science Department, National Institutes of Health (NIH), Bethesda, MD 20892, United States.
  • Sasa Grbic
  • Sebastian Vogt
    X-Ray Products, Siemens Healthineers, Malvern, PA.

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