A team of researchers has found that Artificial Intelligence (AI) can predict risk of new atrial fibrillation (AF) and AF-related stroke.

Atrial fibrillation is the most common cardiac arrhythmia and is associated with numerous health risks, including stroke and death. The study, published in the journal Circulation, used electrical signals from the heart--measured from a 12-lead electrocardiogram (ECG) to identify patients who are likely to develop AF, including those at risk for AF-related stroke. "Not only can we now predict who is at risk of developing atrial fibrillation, but this work shows that the high risk prediction precedes many AF-related strokes," said researcher Brandon Fornwalt from Geisinger Health System in the US. "With that kind of information, we can change the way these patients are screened and treated, potentially preventing such severe outcomes. This is huge for patients," Brandon added.

To develop their model, the team used 1.6 million ECGs from 430,000 patients over 35 years of patient care at Geisinger. These data were used to train a deep neural network -- a specialised class of artificial intelligence -- to predict, among patients without a previous history of AF, who would develop AF within 12 months. The neural network performance exceeded that of current clinical models for predicting AF risk. Furthermore, 62 percent of patients without known AF who experienced an AF-related stroke within three years were identified as high risk by the model before the stroke occurred.