AI Medical Compendium Journal:
Journal of the American College of Cardiology

Showing 11 to 20 of 31 articles

Deep Learning-Enabled Assessment of Left Heart Structure and Function Predicts Cardiovascular Outcomes.

Journal of the American College of Cardiology
BACKGROUND: Deep learning interpretation of echocardiographic images may facilitate automated assessment of cardiac structure and function.

Deep Learning Electrocardiographic Analysis for Detection of Left-Sided Valvular Heart Disease.

Journal of the American College of Cardiology
BACKGROUND: Valvular heart disease is an important contributor to cardiovascular morbidity and mortality and remains underdiagnosed. Deep learning analysis of electrocardiography (ECG) may be useful in detecting aortic stenosis (AS), aortic regurgita...

Care Models for Acute Chest Pain That Improve Outcomes and Efficiency: JACC State-of-the-Art Review.

Journal of the American College of Cardiology
Existing assessment pathways for acute chest pain are often resource-intensive, prolonged, and expensive. In this review, the authors describe existing chest pain pathways and current issues at the patient and system level, and provide an overview of...

Assessment of Disease Status and Treatment Response With Artificial Intelligence-Enhanced Electrocardiography in Obstructive Hypertrophic Cardiomyopathy.

Journal of the American College of Cardiology
AI analysis of HCM ECGs correlates with longitudinal hemodynamic, cardiac structural and laboratory markers in obstructive HCM patients.

Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review.

Journal of the American College of Cardiology
The role of physicians has always been to synthesize the data available to them to identify diagnostic patterns that guide treatment and follow response. Today, increasingly sophisticated machine learning algorithms may grow to support clinical exper...

Machine Learning Improves Cardiovascular Risk Definition for Young, Asymptomatic Individuals.

Journal of the American College of Cardiology
BACKGROUND: Clinical practice guidelines recommend assessment of subclinical atherosclerosis using imaging techniques in individuals with intermediate atherosclerotic cardiovascular risk according to standard risk prediction tools.

Machine Learning Assessment of Left Ventricular Diastolic Function Based on Electrocardiographic Features.

Journal of the American College of Cardiology
BACKGROUND: Left ventricular (LV) diastolic dysfunction is recognized as playing a major role in the pathophysiology of heart failure; however, clinical tools for identifying diastolic dysfunction before echocardiography remain imprecise.