Artificial intelligence (AI) has the potential to transform every facet of cardiovascular practice
and research. The exponential rise in technology powered by AI is defining new frontiers in …

Twelve lead electrocardiogram signals capture unique fingerprints about the body's
biological processes and electrical activity of heart muscles. Machine learning and deep …

Early detection is critical to achieving improved treatment outcomes for child patients with
congenital heart diseases (CHDs). Therefore, develo** effective CHD detection …

Abstract Machine learning (ML) refers to computational algorithms that iteratively improve
their ability to recognize patterns in data. The digitization of our healthcare infrastructure is …

Electrocardiography (ECG), improved by artificial intelligence (AI), has become a potential
technique for the precise diagnosis and treatment of cardiovascular disorders. The …

Abstract Machine learning (ML) methods for the analysis of electrocardiography (ECG) data
are gaining importance, substantially supported by the release of large public datasets …

To achieve synchronous repair and real‐time monitoring the infarcted myocardium based on
an integrated ion‐conductive hydrogel patch is challenging yet intriguing. Herein, a novel …

Background Biological age is a measure of health that offers insights into ageing. The
existing age clocks, although valuable, often trade off accuracy and interpretability. We …

Electrocardiogram (ECG) is a non-invasive approach to capture the overall electrical activity
produced by the contraction and relaxation of the cardiac muscles. It has been established …

Objective The biological age progression of the heart varies from person to person. We
developed a deep learning model (DLM) to predict the biological age via ECG to explore its …