Atrial fibrillation (AF) affects more than 30 million individuals worldwide, making it the most
prevalent cardiac arrhythmia on a global scale. This systematic review summarizes recent …

Background Artificial intelligence (AI) has revolutionized numerous industries, enhancing
efficiency, scalability, and insight generation. In cardiology, particularly through …

Objectives To use pericardial adipose tissue (PAT) radiomics phenoty** to differentiate
existing and predict future heart failure (HF) cases in the UK Biobank. Methods PAT …

Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECG) has the
potential to transform diagnosis and estimate the prognosis of not only cardiac but …

Arrhythmogenic Cardiomyopathy (ACM) is a rare cardiac genetic disease that can lead to
severe cardiac structural and electrical abnormalities. Diagnosing ACM includes several …

Data from a single study are rarely sufficient to comprehensively understand a phenomenon
of interest or to build predictive models with statistical power. Meta-analyses, including …

Atrial fibrillation (AF), a prevalent cardiac arrhythmia, is associated with increased morbidity
and mortality worldwide. Stroke, the leading cause of serious disability in the United States …

Objectives To evaluate the feasibility of utilizing cardiac computer tomography (CT) images
for extracting the radiomic features of the myocardium at the junction between the left atrial …

Ischemic heart disease (IHD) is the main cause of death globally. The coexistence of IHD
with atrial fibrillation (AF) can result in a reduced lifespan and severe disabilities. Despite the …

Data from a single study are rarely sufficient to comprehensively understand a phenomenon
of interest or to build predictive models with statistical power. Meta-analyses, including …