CheXED: Comparison of a Deep Learning Model to a Clinical Decision Support System for Pneumonia in the Emergency Department.

Journal: Journal of thoracic imaging
Published Date: Sep 23, 2021

Abstract

PURPOSE: Patients with pneumonia often present to the emergency department (ED) and require prompt diagnosis and treatment. Clinical decision support systems for the diagnosis and management of pneumonia are commonly utilized in EDs to improve patient care. The purpose of this study is to investigate whether a deep learning model for detecting radiographic pneumonia and pleural effusions can improve functionality of a clinical decision support system (CDSS) for pneumonia management (ePNa) operating in 20 EDs.

Authors

  • Jeremy A Irvin
    Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA, 94305, USA. jirvin16@cs.stanford.edu.
  • Anuj Pareek
    Stanford University, Center for Artificial Intelligence in Medicine & Imaging, Stanford, CA, 94304, US.
  • Jin Long
    Center for Artificial Intelligence in Medicine and Imaging, Stanford University, 1701 Page Mill Road, Palo Alto, CA, 94304, USA.
  • Pranav Rajpurkar
    Harvard Medical School, Department of Biomedical Informatics, Cambridge, MA, 02115, US.
  • David Ken-Ming Eng
    AIMI Center, Stanford University, Stanford.
  • Nishith Khandwala
    Stanford University.
  • Peter J Haug
    Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Salt Lake City, UT 84108, USA; Homer Warner Research Center, Intermountain Healthcare, 5121 South Cottonwood Street, Murray, UT 84107, USA.
  • Al Jephson
    Division of Pulmonary and Critical Care Medicine.
  • Karen E Conner
    Department of Radiology, Intermountain Medical Center, Salt Lake City, UT.
  • Benjamin H Gordon
    Department of Radiology, Intermountain Medical Center, Salt Lake City, UT.
  • Fernando Rodriguez
    Department of Radiology, Intermountain Medical Center, Salt Lake City, UT.
  • Andrew Y Ng
  • Matthew P Lungren
  • Nathan C Dean
    Division of Respiratory, Critical Care, and Occupational Pulmonary Medicine, University of Utah.

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