AI-Guided Screening Uses ECG Data to Detect Hidden Risk Factor for Stroke

In the digitally-enabled, decentralized study, researchers at Mayo Clinic (Rochester, MN, USA) enrolled 1,003 patients for continuous monitoring and used another 1,003 patients from usual care as real-world controls. The findings showed that AI can indeed identify a subgroup of high-risk patients who would benefit more from further intensive heart monitoring to detect atrial fibrillation, supporting an AI-guided targeted screening strategy. Earlier research had already developed an AI algorithm to identify patients with a high likelihood of previously unknown atrial fibrillation. The algorithm for detecting atrial fibrillation in normal sinus rhythm from an ECG is licensed to Anumana Inc. (Cambridge, MA, USA) and can identify patients who, even though they are in normal rhythm on the day of the ECG, may have an increased risk of undetected episodes of atrial fibrillation at other times. Such patients can then undergo additional monitoring to confirm the diagnosis.

"We believe that atrial fibrillation screening has great potential, but currently the yield is too low and the cost is too high to make widespread screening a reality," says Peter Noseworthy, M.D., a cardiac electrophysiologist at Mayo Clinic and lead author of the study. "This study demonstrates that an AI-ECG algorithm can help target screening to patients who are most likely to benefit."

"The study shows that an AI algorithm can select a subgroup of older adults who might benefit more from intensive monitoring. If this new strategy is broadly implemented, it could reduce undiagnosed atrial fibrillation, and prevent stroke and death in millions of patients across the globe," added Xiaoxi Yao, Ph.D., a health outcomes researcher in the Department of Cardiovascular Medicine and the Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery and senior author of the study.

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