AI-enhanced 12-lead ECG
Conditions
Brief summary
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.
Interventions
None listed
Sponsors
Toho University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 99 Years
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| the 1-2-year composite endpoint of all-cause death and major adverse cardiac events (MACE), including AF recurrence | one-two year |
Countries
Japan
Outcome results
None listed