Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk ...

Heart failure is a leading cause of hospitalization and long-term disability, with many individuals progressing from subclinical disease to overt symptoms ...

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...

Integrating male sex, RBBB, and haemoglobin and glucose levels into the HEART score improves its ability to predict significant coronary artery disease on CCTA in the emergency department setting.

FIU Researchers are training AI to detect heart conditions, like aortic stenosis and heart failure, by analyzing heart sound data to improve early diagnosis and risk prediction. The future of heart ...

Many people have no idea what their risk is—until it's too late.

COMET, a novel machine learning framework, integrates EHR data and omics analyses using transfer learning, significantly enhancing predictive modeling and uncovering biological insights from small ...

Current models of mortality risk after heart failure (HF) rely primarily on cardiac-specific clinical variables and may ...

Heart specialists at Mayo Clinic today presented new research at the 2026 Society of Thoracic Surgeons Annual Meeting that redo surgery for adults with congenital heart disease (CHD) remains high-risk ...