RecruitingNCT07347691

AI-Based Prediction of Atrial Fibrillation in ESUS Patients With ICM

Predicting Atrial Fibrillation in Patients With Post-implantable Cardiac Monitor Implementation : A Prospective, Long-term Follow-up Study Using Comprehensive AI ECG Analysis : Multicenter Prospective Study

Sponsor

Inha University Hospital

Enrollment

92 participants

Start Date

Nov 19, 2025

Study Type

OBSERVATIONAL

Conditions

Embolic Stroke of Undetermined Source

Summary

This study investigates patients with Embolic Stroke of Undetermined Source (ESUS) who have received an Implantable Cardiac Monitor (ICM). The main purpose is to evaluate the predictive value of an Artificial Intelligence ECG analysis tool, named SmartECG-AF. Participants will be classified into two groups based on the AI analysis: a "High Risk" group and a "Low to Intermediate Risk" (control) group. The study aims to compare the incidence rate of atrial fibrillation (AF) events over time between these two groups. Additionally, the study will analyze the relationship between the AI-predicted risk levels and the occurrence of major cardiovascular events during the follow-up period.

Eligibility

Min Age: 30 Years

Inclusion Criteria5

Patients aged 30 years or older.
Patients diagnosed with Embolic Stroke of Undetermined Source (ESUS) who have undergone or are scheduled for Implantable Cardiac Monitor (ICM) implantation.
Patients who have undergone at least one 12-lead ECG examination within 2 weeks before or after the date of ICM implantation.
Patients maintaining Sinus Rhythm on ECG at the time of enrollment.
Patients who have voluntarily signed the informed consent form.

Exclusion Criteria3

Patients diagnosed with Atrial Fibrillation (AF) at least once prior to the date of enrollment.
Patients whose ICM battery status is at Elective Replacement Interval (ERI), making recording impossible.
Patients whose ECGs cannot be analyzed by the AI algorithm (SmartECG-AF) due to severe artifacts or noise, or are incompatible with digital analysis.