Identification of Hypertrophic Cardiomyopathy on Electrocardiographic Images with Deep Learning.

Journal: medRxiv : the preprint server for health sciences
Published Date: Dec 28, 2023

Abstract

BACKGROUND: Hypertrophic cardiomyopathy (HCM) affects 1 in every 200 individuals and is the leading cause of sudden cardiac death in young adults. HCM can be identified using an electrocardiogram (ECG) raw voltage data and deep learning approaches, but their point-of-care application is limited by the inaccessibility of these signal data. We developed a deep learning-based approach that overcomes this limitation and detects HCM from images of 12-lead ECGs across layouts.

Authors

  • Veer Sangha
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
  • Lovedeep Singh Dhingra
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
  • Evangelos Oikonomou
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
  • Arya Aminorroaya
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
  • Nikhil V Sikand
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
  • Sounok Sen
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
  • Harlan M Krumholz
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
  • Rohan Khera
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.

Keywords

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