Making Artificial Intelligence Lemonade Out of Data Lemons: Adaptation of a Public Apical Echo Database for Creation of a Subxiphoid Visual Estimation Automatic Ejection Fraction Machine Learning Algorithm.

Journal: Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
Published Date: Nov 24, 2021

Abstract

OBJECTIVES: A paucity of point-of-care ultrasound (POCUS) databases limits machine learning (ML). Assess feasibility of training ML algorithms to visually estimate left ventricular ejection fraction (EF) from a subxiphoid (SX) window using only apical 4-chamber (A4C) images.

Authors

  • Michael Blaivas
    Department of Emergency Medicine, University of South Carolina School of Medicine, Columbia, South Carolina, USA.
  • Laura N Blaivas
    Michigan State University, East Lancing, Michigan, USA.
  • Kendra Campbell
    Department of Emergency Medicine, Harbor-UCLA Medical Center, Torrance, CA, USA.
  • Joseph Thomas
    Department of Cardiology, Harbor-UCLA Medical Center, Torrance, CA, USA.
  • Sonia Shah
    Department of Cardiology, Harbor-UCLA Medical Center, Torrance, CA, USA.
  • Kabir Yadav
    Department of Emergency Medicine, Harbor-UCLA Medical Center, Torrance, CA.
  • Yiju Teresa Liu
    Department of Emergency Medicine, Harbor-UCLA Medical Center, Torrance, CA, USA.

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