This paper is in the following e-collection/theme issue:

Reviews on Cardiovascular Health and Medicine (32) e-Cardiology and Telemonitoring in Cardiology (222) Machine Learning (2081) Cardiac Disease Management (157) Decision Support for Health Professionals (1548) New Methods (1137) Novel Sensors and Data Acquisition Methods in Cardiology (225) Cardiovascular Disease Prevention (207) Cardiac Arrhythmia, Atrial Fibrillation (115) Cardiac Risk and Cardiac Risk Calculators (102)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/60697, first published .
The Role of Machine Learning in the Detection of Cardiac Fibrosis in Electrocardiograms: Scoping Review

The Role of Machine Learning in the Detection of Cardiac Fibrosis in Electrocardiograms: Scoping Review

The Role of Machine Learning in the Detection of Cardiac Fibrosis in Electrocardiograms: Scoping Review

Authors of this article:

Julia Handra1, 2 Author Orcid Image ;   Hannah James1, 2 Author Orcid Image ;   Ashery Mbilinyi2 Author Orcid Image ;   Ashley Moller-Hansen2 Author Orcid Image ;   Callum O'Riley2 Author Orcid Image ;   Jason Andrade3 Author Orcid Image ;   Marc Deyell3 Author Orcid Image ;   Cameron Hague4 Author Orcid Image ;   Nathaniel Hawkins3 Author Orcid Image ;   Kendall Ho5 Author Orcid Image ;   Ricky Hu1 Author Orcid Image ;   Jonathon Leipsic4 Author Orcid Image ;   Roger Tam2 Author Orcid Image
Article Authors Cited by (1) Tweetations Metrics

Journals

  1. Tran T, Le P, Yang S, Luong T, Kim J. Rewiring the scar: translational advances in cardiac fibrosis. The Korean Journal of Physiology & Pharmacology 2025;29(6):669 View