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Review
. 2024 Aug 23;14(17):1839.
doi: 10.3390/diagnostics14171839.

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

Luiza Camelia Nechita  1 , Aurel Nechita  1 , Andreea Elena Voipan  2 , Daniel Voipan  2 , Mihaela Debita  1 , Ana Fulga  1 , Iuliu Fulga  1 , Carmina Liana Musat  1
Affiliations
Review

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

Luiza Camelia Nechita et al. Diagnostics (Basel). .

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.

Keywords: COVID-19; artificial intelligence; cardio-oncology; cardiology; convolutional neural networks (CNNs); deep learning (DL); diagnosis; electrocardiography; machine learning (ML); risk prediction.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Systematic literature review process for AI-ECG applications in cardiology and COVID-19.
Figure 2
Figure 2
Comparison of traditional machine learning and deep learning approaches.
Figure 3
Figure 3
Overview of supervised, unsupervised, and reinforcement ML techniques.
See this image and copyright information in PMC

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