ROCHESTER, Minnesota -- A new Mayo Clinic research study shows that artificial intelligence (AI) can detect the signs of an irregular heart rhythm -- atrial fibrillation (AF) -- in an electrocardiogram (EKG), even if the heart is in normal rhythm at the time of a test. In other words, the AI-enabled EKG can detect recent atrial fibrillation that occurred without symptoms or that is impending, potentially improving treatment options. This research could improve the efficiency of the EKG, a noninvasive and widely available method of heart disease screening. The findings and an accompanying commentary are published in The Lancet.
While common, atrial fibrillation often is fleeting. Therefore, it is challenging to diagnose. Atrial fibrillation may not occur during a standard 10-second, 12-lead EKG, and people are often unaware of its presence. Prolonged monitoring methods, such as a loop recorder, require a procedure and are expensive.
Accuracy and timeliness are important in making an atrial fibrillation diagnosis. Left undetected, atrial fibrillation can cause stroke, heart failure and other cardiovascular disease. Knowing that a patient has atrial fibrillation helps direct treatment with blood thinners, notes Paul Friedman, M.D., chair of the Department of Cardiovascular Medicine at Mayo Clinic. Dr. Friedman, who is a cardiac electrophysiologist, is the study's senior author.
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"When people come in with a stroke, we really want to know if they had AF in the days before the stroke, because it guides the treatment," says Dr. Friedman. "Blood thinners are very effective for preventing another stroke in people with AF. But for those without AF, using blood thinners increases the risk of bleeding without substantial benefit. That's important knowledge. We want to know if a patient has AF."
Using approximately 450,000 EKGs of the over 7 million EKGs in the Mayo Clinic digital data vault, researchers trained AI to identify subtle differences in a normal EKG that would indicate changes in heart structure caused by atrial fibrillation. These changes are not detectable without the use of AI.
Researchers then tested the AI on normal-rhythm EKGs from a group of 36,280 patients, of whom 3,051 were known to have atrial fibrillation. The AI-enabled EKG correctly identified the subtle patterns of atrial fibrillation with 90% accuracy.
Dr. Friedman says that he is surprised by the findings of this research. If proven out, he said, AI-guided EKGs could direct the right treatment for disease caused by atrial fibrillation, even without symptoms. Moreover, this technology can be processed using a smartphone or watch, making it readily available on a large scale.