Do you remember the anticipation around renal denervation? And then the ACC.14 presentation of the SYMPLICITY HTN-3 results, where the excitement about a new way to treat resistant hypertension seemed to crash?

A machine learning tool developed by researchers at Michigan State University can accurately predict an individual’s height within an inch and may offer more precise CVD and cancer risk assessment in the future, according to a report featured this month in Genetics.

A New Jersey hospital is testing an artificial intelligence (AI) device designed to alert clinicians to bleeding episodes during endovascular procedures.

A machine learning model developed by scientists at the Francis Crick Institute in London can more accurately predict risk of death in patients with heart disease than leading models designed by medical experts, according to a study published in PLOS One.

A deep-learning software that can automatically calculate left ventricular ejection fraction (LVEF) with less variability than a cardiologist recently received approval from the U.S. Food and Drug Administration (FDA).

Wearable sensors and artificial intelligence (AI) could help predict the onset of cardiovascular disease by assessing an individual's changes in aerobic responses, according to new research published on Feb. 23 in the Journal of Applied Physiology.

The FDA cleared Viz.ai’s clinical support tool on Feb. 13, allowing the software that alerts clinicians to the possibility of a stroke to be marketed in the United States.

KFAR MALAL, Israel, Feb. 5, 2018 — Medial EarlySign, a leader of machine-learning based solutions to improve non-communicable disease management, today announced the results of an additional clinical data study in the domain of diabetes — identifying diabetic patients who are at highest risk for having renal dysfunction within one year.