Diagnostic accuracy of an automated classifier for the detection of pleural effusions in patients undergoing lung ultrasound.

Journal: The American journal of emergency medicine
PMID: 39874677

Abstract

RATIONALE: Lung ultrasound, the most precise diagnostic tool for pleural effusions, is underutilized due to healthcare providers' limited proficiency. To address this, deep learning models can be trained to recognize pleural effusions. However, current models lack the ability to diagnose effusions in diverse clinical contexts, which presents significant challenges.

Authors

  • Rushil Chaudhary
    Department of Medicine, Western University, London, Ontario, Canada.
  • Jordan Ho
    Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
  • Delaney Smith
    Faculty of Mathematics, University of Waterloo, Waterloo, ON, Canada.
  • Saad Hossain
    Faculty of Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
  • Jaswin Hargun
    Faculty of Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
  • Blake VanBerlo
    Schulich School of Medicine, University of Western Ontario, London, Ontario, Canada.
  • Niall Murphy
    Nuritas Ltd., Joshua Dawson House, Dawson St, Dublin 2, Ireland.
  • Ross Prager
    Division of Critical Care Medicine, Western University, London, ON, Canada.
  • Kiran Rikhraj
    Department of Emergency Medicine, The University of British Columbia, Vancouver, BC V5Z 1M9, Canada.
  • Jared Tschirhart
    Schulich School of Medicine and Dentistry, Western University, London, ON N6A 5C1, Canada.
  • Robert Arntfield
    Department of Critical Care Medicine, Western University, London, Ontario, Canada.

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