Atrial fibrillation (AF) is a major cause of heart failure and ischemic stroke, making early detection and intervention critically important. However, timely ECG recording during paroxysmal episodes is often difficult, leading to delayed diagnosis. Recently, an AI-enhanced 12-lead ECG equipped with a "hidden AF risk estimation" function has been introduced. This technology analyzes sinus rhythm ECGs and stratifies the likelihood of prior AF into four risk categories. Although this novel approach may facilitate earlier AF detection and optimize the timing of therapeutic intervention, its clinical accuracy and real-world utility remain insufficiently validated. Therefore, this multicenter study aims to evaluate the diagnostic performance and clinical usefulness of AI-based AF risk assessment and to clarify its association with subsequent AF incidence and patient outcomes.

Inclusion Criteria

1. Age ≥18 years
2. Patients with atrial fibrillation or atrial tachycardia (AF/AT) in whom sinus rhythm is maintained or can be confirmed at the time of ECG recording

Exclusion Criteria

1. Age <18 years
2. History of long-standing persistent or permanent atrial fibrillation
3. Frequent premature beats preventing acquisition of a sinus rhythm ECG
4. Patients with no clinical indication to suspect atrial fibrillation

The study will be conducted at 6 centers throughout Japan, with the goal of enrolling a total of approximately 300-350 cases.