We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

HospiMedica

Download Mobile App
Recent News MEDICA 2022 COVID-19 AI Critical Care Surgical Techniques Patient Care Health IT Business

Perry Health

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

By HospiMedica International staff writers
Posted on 30 Sep 2022

Atrial fibrillation is an irregular heartbeat that can lead to blood clots that may travel to the brain and cause a stroke, but it is largely underdiagnosed. Electrocardiograms (ECGs) are commonly performed for a variety of diagnostics, but since atrial fibrillation can be fleeting, the chance of catching an episode on a single 10-second ECG tracing is low. Patients can undergo continuous or intermittent cardiac monitoring approaches that have higher detection rates, but they are too resource-intensive to apply to everyone and can be burdensome and expensive for patients. Now, researchers using artificial intelligence (AI) to evaluate patients’ ECGs in a targeted strategy to screen for atrial fibrillation found that AI identified new cases that would not have come to clinical attention during routine care.

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.


Image: AI-guided targeted screening strategy could reduce undiagnosed cases of atrial fibrillation (Photo courtesy of Pexels)
Image: AI-guided targeted screening strategy could reduce undiagnosed cases of atrial fibrillation (Photo courtesy of Pexels)

"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.

Related Links:
Mayo Clinic 
Anumana Inc. 


BMP Whole Blood Analyzer: GEM Premier ChemSTAT
Gold Supplier
Real-Time PCR System
Applied Biosystems QuantStudio 7 Pro Dx
New
DNA/RNA Extraction Buffer
Enzymatic DNA/RNA Extraction Buffer
New
Antimicrobial Susceptibility Testing Software
MICRONAUT6 Software

Latest AI News