Validation of American Society of Echocardiography Guideline-Recommended Parameters of Right Ventricular Dysfunction Using Artificial Intelligence Compared With Cardiac Magnetic Resonance Imaging

Hsia, Brian; Lai, Ashton; Singh, S. P.; Samtani, Rajeev; Bienstock, Solomon; Liao, Steve; Stern, Eric H.; LaRocca, Gina; Sanz, Javier; Croft, Lori B.; Carrasco, Susana; Rosenmann, David; DeMaria, Anthony N.; Stone, Gregg W.; Goldman, Martin E.

doi:10.1016/j.echo.2023.05.015

Cited by 6 publications

(1 citation statement)

References 33 publications

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“…The software displayed excellent sensitivity and a negative predictive value for detecting RV dysfunction, although it showed low specificity and positive predictive value. Nonetheless, its ability to reliably exclude significant RV dysfunction should be emphasized [ 38 ]. Recently, a machine learning model trained on data from patients who underwent right-sided heart catheterization and transthoracic echocardiography was used to predict pulmonary hypertension (PH) through echocardiography.…”

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%