Skip to main content

MINI REVIEW article

Front. Cardiovasc. Med.
Sec. Cardiovascular Imaging
Volume 11 - 2024 | doi: 10.3389/fcvm.2024.1457498
This article is part of the Research Topic Artificial Intelligence for Myocardial Tissue Characterization in Cardiac Magnetic Resonance View all articles

Myocardial Infarction Detection with Machine Learning Applications in Contrast-Free Cardiovascular Magnetic Resonance Imaging

Provisionally accepted
Vedat Çiçek Vedat Çiçek *Ulas Bagci Ulas Bagci
  • Feinberg School of Medicine, Northwestern University, Chicago, United States

The final, formatted version of the article will be published soon.

    Cardiovascular magnetic (CMR) resonance is a versatile tool for diagnosing cardiovascular diseases. While gadolinium-based contrast agents are the gold standard for identifying myocardial infarction (MI), their use is limited in patients with allergies or impaired kidney function, affecting a significant portion of the MI population. This has led to a growing interest in developing artificial intelligence (AI)-powered CMR techniques for MI detection without contrast agents. This mini-review focuses on recent advancements in AI-powered contrast-free CMR for MI detection. We explore various AI models employed in the literature and delve into their strengths and limitations, paving the way for a comprehensive understanding of this evolving field.

    Keywords: Cardiac magnet resonance imaging (CMR), deep learning - artificial intelligence, Machine Lear ning, contrast free, Late gadolinium contrast agent enhancement

    Received: 30 Jun 2024; Accepted: 29 Oct 2024.

    Copyright: © 2024 Çiçek and Bagci. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Vedat Çiçek, Feinberg School of Medicine, Northwestern University, Chicago, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.