AI-enabled ECGs predict atrial fibrillation 10 years before diagnosis, Mayo Clinic study finds

Rochester, Minn.-based Mayo Clinic found that an AI-powered electrocardiography is capable of predicting atrial fibrillation up to 10 years before clinical diagnosis, according to a study published in Mayo Clinic Proceedings.

A population-based study from Mayo Clinic, reviewed sinus-rhythm ECGs of 3,729 patients with a median age of 74 years who were enrolled in the Mayo Clinic Study of Aging between 2004 and 2020, using an AI-ECG algorithm. 

The study found evidence that the algorithm helped identify patients at greater risk of cognitive decline, accurately flagged individuals at risk of atrial fibrillation, the most common cardiac rhythm abnormality, and identified future risk of stroke.  

"This study finds that artificial intelligence-enabled electrocardiography acquired during normal sinus rhythm was associated with worse baseline cognition and gradual decline in global cognition and attention," said Jonathan Graff-Radford, MD, corresponding author of the study and neurologist at Mayo Clinic. "The findings raise the question whether initiation of anticoagulation is an effective and safe preventive strategy in individuals with a high AI-ECG algorithm score for reducing the risk of stroke and cognitive decline."

Application of AI-ECG algorithms may be another way to screen individuals to determine risk of atrial fibrillation, but also to identify future risk of cognitive decline and stroke, according to the study.

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