Detecting heart irregularities such as atrial fibrillation can be a tricky business, as the symptoms may not be apparent when the patient is undergoing an electrocardiogram (EKG).

But according to a recent Mayo Clinic research study, new, AI-enabled EKG technology can “detect recent atrial fibrillation that occurred without symptoms or that is impending, potentially improving treatment options.”

The problem for clinicians is that “a-fib” can be a fleeting phenomenon that occurs outside the parameters of a standard EKG.  But by 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 subsequently tested the AI on normal-rhythm EKGs from a group of 36,280 patients, over 3,000 of whom were known to have atrial fibrillation. “The AI-enabled EKG correctly identified the subtle patterns of atrial fibrillation with 90% accuracy.”

According to Paul Friedman, MD, chair of the Department of Cardiovascular Medicine at Mayo Clinic and the study’s senior author, "An EKG will always show the heart's electrical activity at the time of the test, but this is like looking at the ocean now and being able to tell that there were big waves yesterday. AI can provide powerful information about the invisible electrical signals that our bodies give off with each heartbeat -- signals that have been hidden in plain sight."

He added, “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. 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.”

According to Jeroen Hendriks, Ph.D., of the University of Adelaide in Australia, who, with Larissa Fabritz, MD, of the UK’s University of Birmingham, wrote the study's commentary published in The Lancet, "Rather than finding the needle in the haystack by prolonged monitoring, the (study’s) authors basically suggest that AI will be able to judge by looking at the haystack if it has a needle hidden in it.”