Somewhere in Peter Maercklein's heartbeat was an abnormality no one could find. He survived a stroke 15 years ago, but doctors never saw anything alarming on follow-up electrocardiograms. Then, one day last fall, an artificial intelligence algorithm read his EKGs and spotted something else: a ripple in the calm that indicated an elevated risk of atrial fibrillation. Specifically, the algorithm, created by physicians at Mayo Clinic, found Maercklein had an 81.49% probability of experiencing A-fib, a quivering or irregular heartbeat that can lead to heart failure and stroke. Just days later, after Maercklein agreed to participate in a research study, a wearable Holter monitor recorded an episode of A-fib while he was walking on a treadmill.

Artificial intelligence (AI) can detect signs of existing or emerging A-fib in ECGs that exhibit normal sinus rhythm, Mayo Clinic researchers have found. Their retrospective analysis, published online yesterday in the Lancet, reports a high degree of accuracy with only one ECG, and this accuracy increases when AI is applied to multiple ECGs from the same patient. "A very common clinical scenario is that someone comes to the hospital with an ischemic stroke, and we want to know whether they have atrial fibrillation," senior author Paul A. Friedman, MD (Mayo Clinic, Rochester, MN), noted to TCTMD. "We have done previous work using neural networks, machine learning, that found that it was extremely powerful in detecting subtle patterns [in ECG tracings], and we wondered: If someone had atrial fibrillation yesterday, is there any way that it might leave a trace of a finding on an ECG today that's too subtle for a human to read but a computer could pick up?" To find out, the investigators, led by Zachi I. Attia, MSc, and Peter A. Noseworthy, MD, drew upon records in the Mayo Clinic Digital Data Vault.