Checking how well AI-enhanced ECG spots hidden atrial fibrillation risk
Studies an AI-enhanced ECG that estimates atrial fibrillation risk in adults.
Plain-language title generated from the registry record
Registry title: Evaluating the Accuracy and Practical Utility of AI-Enhanced 12-Lead ECG
Key facts
- Where
- Ōta-ku, JapanSee the site
Could this be for you?
Answer for yourself. This is not a decision: only the study team can say whether you can take part.
- Are you between 18 and 99 years old?
Source: NCT07316231, who can take part
Where in Japan
- Toho UniversityŌta-ku, JapanTaking people
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Toho University
- Trial code
- NCT07316231
- Record updated
- 7 January 2026
- Original description
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.Read full original descriptionShow less
Toho Multicenter Registry for Evaluating the Accuracy and Practical Utility of AI-Enhanced 12-Lead ECG in Monitoring Atrial Fibrillation
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: Age ≥18 years; 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: History of long-standing persistent or permanent atrial fibrillation; Age <18 years; Frequent premature beats preventing acquisition of a sinus rhythm ECG; Patients with no clinical indication to suspect atrial fibrillation.