You are free to share this article under the Attribution 4.0 International license. A new artificial intelligence-based approach can predict if and when a patient could die of a heart attack. The technology, built on raw images of patient's diseased hearts and patient backgrounds, significantly improves on doctor's predictions and stands to revolutionize clinical decision making and increase survival from sudden and lethal cardiac arrhythmias, one of medicine's deadliest and most puzzling conditions. "Sudden cardiac death caused by arrhythmia accounts for as many as 20% of all deaths worldwide and we know little about why it's happening or how to tell who's at risk," says senior author Natalia Trayanova, a professor of biomedical engineering and medicine at Johns Hopkins University. "There are patients who may be at low risk of sudden cardiac death getting defibrillators that they might not need and then there are high-risk patients that aren't getting the treatment they need and could die in the prime of their life. What our algorithm can do is determine who is at risk for cardiac death and when it will occur, allowing doctors to decide exactly what needs to be done."

For patients, the act of getting the scan is no different, but the results are sent off to Oxford, analysed by Caristo, and returned within a handful of days. The difference between a regulation coronary CT scan and the new analysis lies in what comes after the scan itself, when the images are inputted into CaRi-Heart. Scientists say that although plaque build up is a serious problem, around half of all heart attacks do not occur in fully-blocked arteries. The other half are caused when small pieces of plaque rupture, releasing cholesterol into the blood which triggers a clot and ultimately leads to a heart attack. These are impossible to predict with current CT scans.

Artificial intelligence could be used to predict those at risk of a fatal heart attack up to five years in advance, new research has found. Experts at the University of Oxford have developed a "fingerprint", or biomarker, using machine learning. When a patient is admitted to hospital with chest pain, it's standard procedure for a coronary CT angiogram (CCTA) to be performed. If no narrowing of the arteries is detected – about 75% of cases – then the patient is sent home – yet some of them suffer a heart attack in the future. There's currently no method routinely used by doctors to spot all underlying red flags of a future heart attack.