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Review
. 2024 Mar 9;16(3):e55869.
doi: 10.7759/cureus.55869. eCollection 2024 Mar.

Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review

Naiela E Almansouri  1 , Mishael Awe  2 , Selvambigay Rajavelu  3 , Kudapa Jahnavi  4 , Rohan Shastry  5 , Ali Hasan  6 , Hadi Hasan  7 , Mohit Lakkimsetti  8 , Reem Khalid AlAbbasi  9 , Brian Criollo Gutiérrez  10 , Ali Haider  11
Affiliations
Review

Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review

Naiela E Almansouri et al. Cureus. .

Abstract

Cardiovascular diseases (CVDs) are significant health issues that result in high death rates globally. Early detection of cardiovascular events may lower the occurrence of acute myocardial infarction and reduce death rates in people with CVDs. Traditional data analysis is inadequate for managing multidimensional data related to the risk prediction of CVDs, heart attacks, medical image interpretations, therapeutic decision-making, and disease prognosis due to the complex pathological mechanisms and multiple factors involved. Artificial intelligence (AI) is a technology that utilizes advanced computer algorithms to extract information from large databases, and it has been integrated into the medical industry. AI methods have shown the ability to speed up the advancement of diagnosing and treating CVDs such as heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease, and more. In clinical settings, AI has shown usefulness in diagnosing cardiovascular illness, improving the efficiency of supporting tools, stratifying and categorizing diseases, and predicting outcomes. Advanced AI algorithms have been intricately designed to analyze intricate relationships within extensive healthcare data, enabling them to tackle more intricate jobs compared to conventional approaches.

Keywords: ai in cardiology; artificial intelligence; cardiovascular diseases; efficacy of ai in cardiac medicine; machine learning.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Flowchart depicting the use of artificial intelligence in clinical practice
Reproduced from ref. [13] under the terms and conditions of the Creative Commons CC BY license. Copyright 2023 Springer Nature, The author(s).
Figure 2
Figure 2. Proposed predictive modeling system's whole process
Reproduced from ref. [38] under the terms and conditions of the Creative Commons Attribution (CC BY) license. Copyright 2023 by the authors. Licensee MDPI, Basel, Switzerland
Figure 3
Figure 3. Arrhythmia treatment improved via artificial intelligence
Reproduced with permission from ref. [45]. Copyright 2021 Oxford University Press, The author(s).
Figure 4
Figure 4. Diagram illustrating the many uses of Artificial Intelligence in the field of Cardiology
Reproduced with permission from ref. [73]. Copyright 2022  Elsevier, a division of RELX India, Pvt. Ltd on behalf of Cardiological Society of India.
Figure 5
Figure 5. Application of AI in Peripheral artery disease
Reproduced with permission from ref. [106]. Copyright 2022 by the Society for Vascular Surgery. Published by Elsevier Inc.
See this image and copyright information in PMC

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