Improving detection of obstructive coronary artery disease with an artificial intelligence-enabled electrocardiogram algorithm

Lee, Yin-Hao; Hsieh, Meng-Yen; Chang, Chun-Chin; Tsai, Yi‐Lin; Chou, Ruey‐Hsing; Lu, Hsiao‐Ming; Huang, Po‐Hsun

doi:10.1016/j.atherosclerosis.2023.117238

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…This model achieves diagnostic performance comparable to traditional methods based on cardiovascular risk factors (CVRFs). It could prove to be a valuable clinical tool in outpatient settings to determine which patients might require further diagnostic testing for a stable CAD [ 29 ].…”

Section: Resultsmentioning

confidence: 99%

Future Horizons: The Potential Role of Artificial Intelligence in Cardiology

Patrascanu,

Tutunaru,

Musat

et al. 2024

JPM

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Cardiovascular diseases (CVDs) are the leading cause of premature death and disability globally, leading to significant increases in healthcare costs and economic strains. Artificial intelligence (AI) is emerging as a crucial technology in this context, promising to have a significant impact on the management of CVDs. A wide range of methods can be used to develop effective models for medical applications, encompassing everything from predicting and diagnosing diseases to determining the most suitable treatment for individual patients. This literature review synthesizes findings from multiple studies that apply AI technologies such as machine learning algorithms and neural networks to electrocardiograms, echocardiography, coronary angiography, computed tomography, and cardiac magnetic resonance imaging. A narrative review of 127 articles identified 31 papers that were directly relevant to the research, encompassing a broad spectrum of AI applications in cardiology. These applications included AI models for ECG, echocardiography, coronary angiography, computed tomography, and cardiac MRI aimed at diagnosing various cardiovascular diseases such as coronary artery disease, hypertrophic cardiomyopathy, arrhythmias, pulmonary embolism, and valvulopathies. The papers also explored new methods for cardiovascular risk assessment, automated measurements, and optimizing treatment strategies, demonstrating the benefits of AI technologies in cardiology. In conclusion, the integration of artificial intelligence (AI) in cardiology promises substantial advancements in diagnosing and treating cardiovascular diseases.

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Section: Resultsmentioning

confidence: 99%