AI-Guided Atrial Fibrillation Detection After Ischemic Stroke: Prospective Randomized Trial
Summary
NIH registered a prospective randomized trial (NCT07540065) comparing AI-guided atrial fibrillation risk stratification plus intensified rhythm monitoring (wearable devices, extended ECG patches) against standard care in post-ischemic stroke patients. The trial enrolls an Active Follow-up Group and a Standard Follow-up Group, with conditions including Atrial Fibrillation, Stroke, and Artificial Intelligence. The primary hypothesis is that AI-assisted ECG analysis will significantly increase AF detection rates and enable earlier anticoagulation decisions while reducing unnecessary bleeding risk exposure.
What changed
NIH registered a new prospective randomized clinical trial on ClinicalTrials.gov. The trial investigates whether an AI-guided AF risk stratification approach combined with intensified rhythm monitoring using wearable devices and extended ECG patches increases AF detection rates compared with standard care in patients who have experienced an ischemic stroke.
Affected parties include healthcare providers managing post-stroke care, patients at risk of occult atrial fibrillation, and institutions conducting or referencing this trial for clinical decision-making. The study's findings, once available, may inform the integration of AI-assisted ECG analysis into routine post-stroke care pathways and anticoagulation prescribing practices.
Archived snapshot
Apr 20, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
AI-Assisted Workflow for Occult Atrial Fibrillation Detection After Ischemic Stroke: A Prospective Randomized Trial
N/A NCT07540065 Kind: NA Apr 20, 2026
Abstract
We hypothesize that an AI-guided AF risk stratification approach, particularly when combined with intensified rhythm monitoring using wearable devices and extended ECG patches, will significantly increase AF detection rates compared with standard care. By enabling earlier identification of patients who may benefit from anticoagulation therapy, this strategy has the potential to improve clinical outcomes while minimizing unnecessary exposure to anticoagulant-related bleeding risks. Ultimately, this trial seeks to provide robust clinical evidence supporting the integration of AI-assisted ECG analysis into routine post-stroke care, advancing precision medicine and optimizing resource allocation for patients with ischemic stroke.
Conditions: Atrial Fibrillation, Stroke, Artificial Intelligence
Interventions: Active Follow-up Group, Standard Follow-up Group
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