Tests AI-assisted heart tracing to find atrial fibrillation early
Tests an AI heart-tracing tool in adults with suspected atrial fibrillation.
Plain-language title generated from the registry record
Registry title: A Prospective, Multi-center, Randomized Clinical Trial to Evaluate the Detection of Atrial Fibrillation Using Artificial Intelligence-Enhanced Electrocardiography (SmartECG-AFrisk) Compared With Usual Care in Patients With Suspected Atrial Fibrillation: DEEP-AF
Key facts
- Where
- Seoul, South KoreaSee the site
Where in South Korea
- Yonsei University College of MedicineSeoul, South KoreaNo longer taking part
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
- Yonsei University
- Trial code
- NCT07173673
- Record updated
- 15 September 2025
- Original description
"The DEEP-AF study is a prospective, multi-center, randomized clinical trial evaluating the effectiveness of an artificial intelligence-enhanced electrocardiography algorithm (SmartECG-AFrisk) for early detection of atrial fibrillation (AF) in adults with suspected AF but no prior diagnosis. A total of 1,230 participants will be enrolled across 13 centers in Korea and randomized 1:1 into standard care or AI-guided care arms.Read full original descriptionShow less
A Prospective, Multi-center, Randomized Clinical Trial to Evaluate the Detection of Atrial Fibrillation Using Artificial Intelligence-Enhanced Electrocardiography (SmartECG-AFrisk) Compared With Usual Care in Patients With Suspected Atrial Fibrillation: DEEP-AF
"The DEEP-AF study is a prospective, multi-center, randomized clinical trial evaluating the effectiveness of an artificial intelligence-enhanced electrocardiography algorithm (SmartECG-AFrisk) for early detection of atrial fibrillation (AF) in adults with suspected AF but no prior diagnosis. A total of 1,230 participants will be enrolled across 13 centers in Korea and randomized 1:1 into standard care or AI-guided care arms.
In the standard care arm, diagnostic evaluation follows clinical guidelines with symptom-based use of 12-lead ECG, Holter, or patch ECG. In the AI-guided arm, baseline 12-lead ECGs are analyzed using SmartECG-AFrisk to calculate an AF risk score. Participants are classified as high-risk (score ≥50) or low-risk (<50), and monitoring strategies are determined accordingly, enabling targeted ECG monitoring for high-risk individuals.
The primary objective is to compare the 6-month incidence of newly diagnosed AF between the two arms. Secondary endpoints include AF detection differences between risk groups, healthcare resource utilization per AF diagnosis, anticoagulation initiation rates, major clinical events (stroke, embolism, bleeding, mortality), and patient satisfaction.
This study aims to demonstrate whether integrating AI-driven ECG risk stratification into routine care improves AF detection and optimizes healthcare resource use in real-world clinical practice.
Inclusion: Adults ≥30 years old; - Symptoms suggestive of atrial fibrillation (palpitations, dizziness, syncope, dyspnea, chest discomfort); - No evidence of AF on baseline 12-lead ECG; - No prior history of AF diagnosis.
Exclusion: - Prior diagnosis of atrial fibrillation; - Life expectancy ≤ 1 year.