Deep learning of longitudinal chest X-ray and clinical variables predicts duration on ventilator and mortality in COVID-19 patients.

Journal: Biomedical engineering online
Published Date: Oct 14, 2022

Abstract

OBJECTIVES: To use deep learning of serial portable chest X-ray (pCXR) and clinical variables to predict mortality and duration on invasive mechanical ventilation (IMV) for Coronavirus disease 2019 (COVID-19) patients.

Authors

  • Hongyi Duanmu
    Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY; Department of Computer Science, Stony Brook University, Stony Brook, NY.
  • Thomas Ren
    Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY.
  • Haifang Li
    Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY.
  • Neil Mehta
    Cleveland Clinic, USA.
  • Adam J Singer
    Department of Emergency Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, 11794, USA.
  • Jeffrey M Levsky
    Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
  • Michael L Lipton
    Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
  • Tim Q Duong
    Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York, United States of America.

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