Artificial intelligence-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography: a multicentre study.

Journal: The Lancet. Digital health
PMID: 39890242

Abstract

BACKGROUND: Point-of-care ultrasonography (POCUS) enables cardiac imaging at the bedside and in communities but is limited by abbreviated protocols and variation in quality. We aimed to develop and test artificial intelligence (AI) models to screen for under-diagnosed cardiomyopathies from cardiac POCUS.

Authors

  • Evangelos K Oikonomou
    Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, UK.
  • Akhil Vaid
    Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA.
  • Gregory Holste
    Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA.
  • Andreas Coppi
  • Robert L McNamara
  • Cristiana Baloescu
  • Harlan M Krumholz
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
  • Zhangyang Wang
    Departments of Electrical and Computer Engineering & Computer Science and Engineering Texas A&M University, College Station, TX 77840.
  • Donald J Apakama
    Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States.
  • Girish N Nadkarni
    Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Rohan Khera
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.

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