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AI-Enhanced ECG Applications in Cardiology: Comprehensive Insights from the Current Literature with a Focus on COVID-19 and Multiple Cardiovascular Conditions.

Authors :
Nechita, Luiza Camelia
Nechita, Aurel
Voipan, Andreea Elena
Voipan, Daniel
Debita, Mihaela
Fulga, Ana
Fulga, Iuliu
Musat, Carmina Liana
Source :
Diagnostics (2075-4418). Sep2024, Vol. 14 Issue 17, p1839. 28p.
Publication Year :
2024

Abstract

The application of artificial intelligence (AI) in electrocardiography is revolutionizing cardiology and providing essential insights into the consequences of the COVID-19 pandemic. This comprehensive review explores AI-enhanced ECG (AI-ECG) applications in risk prediction and diagnosis of heart diseases, with a dedicated chapter on COVID-19-related complications. Introductory concepts on AI and machine learning (ML) are explained to provide a foundational understanding for those seeking knowledge, supported by examples from the literature and current practices. We analyze AI and ML methods for arrhythmias, heart failure, pulmonary hypertension, mortality prediction, cardiomyopathy, mitral regurgitation, hypertension, pulmonary embolism, and myocardial infarction, comparing their effectiveness from both medical and AI perspectives. Special emphasis is placed on AI applications in COVID-19 and cardiology, including detailed comparisons of different methods, identifying the most suitable AI approaches for specific medical applications and analyzing their strengths, weaknesses, accuracy, clinical relevance, and key findings. Additionally, we explore AI's role in the emerging field of cardio-oncology, particularly in managing chemotherapy-induced cardiotoxicity and detecting cardiac masses. This comprehensive review serves as both an insightful guide and a call to action for further research and collaboration in the integration of AI in cardiology, aiming to enhance precision medicine and optimize clinical decision-making. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20754418
Volume :
14
Issue :
17
Database :
Academic Search Index
Journal :
Diagnostics (2075-4418)
Publication Type :
Academic Journal
Accession number :
179645247
Full Text :
https://doi.org/10.3390/diagnostics14171839