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AI-Assisted Workflow for Occult Atrial Fibrillation Detection After Ischemic Stroke: A Prospective Randomized Trial
Sponsor: National Taiwan University Hospital
Summary
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.
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
INTERVENTIONAL
Enrollment
400
Start Date
2026-10-01
Completion Date
2028-12-31
Last Updated
2026-04-20
Healthy Volunteers
No
Interventions
Active Follow-up Group
High-frequency electrocardiogram (ECG) monitoring was conducted, using a smartwatch with continuous photoplethysmography (PPG) monitoring, supplemented by an external single-lead ECG patch for 14 days. Participants recorded ECGs for 14 days immediately after discharge. If the smartwatch detected atrial fibrillation, the patch would be automatically applied for 14 days of recording. After recording, the patch was mailed or delivered in person to the research team for analysis.
Standard Follow-up Group
followed a standardized follow-up procedure by a neurologist. Each follow-up visit included a medical history and physical examination, without further cardiac-related monitoring. However, if the patient presented with symptoms of arrhythmia, the physician could consult a cardiologist or perform a preliminary 12-lead ECG.
Locations (1)
National Taiwan University Hospita
Taipei, Taiwan