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.
18 years or older who are acutely hospitalized due to ischemic stroke;
A 12-lead electrocardiogram upon admission showing sinus rhythm;
Previous diagnosis of atrial fibrillation or flutter;
Diagnosis of atrial fibrillation during hospitalization;
Pre-existing cardiac implantable electronic device (IVD);
Expected inability to attend regular outpatient follow-ups after discharge, or to undergo multiple 14-day ECG recordings;