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

NCT ID: NCT07173673

Last Updated: 2025-09-15

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Clinical Phase: NA
• Total Enrollment: 1230 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2025-11-30
• Study Completion Date: 2027-11-30

Brief Summary

"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.

Conditions

Atrial Fibrillation

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: NONE

Study Groups

Usual Care (General Practice)

Symptom-based evaluation by physicians, including 12-lead ECG, Holter monitoring, or patch ECG at least once within 6 months

Group Type: ACTIVE_COMPARATOR

Usual Care (General Practice)

Intervention Type: OTHER

Participants receive routine care based on current clinical guidelines. Symptom-driven evaluation is performed by physicians, including at least one diagnostic test within 6 months such as a standard 12-lead ECG, Holter monitoring, or patch ECG. The choice and frequency of monitoring are determined by physician discretion, reflecting real-world practice patterns.

AI-ECG Guided Care (SmartECG-AFrisk)

Monitoring strategy determined by SmartECG-AFrisk risk score (≥50 = high-risk vs \<50 = low-risk).

Group Type: EXPERIMENTAL

AI-ECG Guided Care (SmartECG-AFrisk)

Intervention Type: DEVICE

Participants undergo SmartECG-AFrisk analysis of baseline 12-lead ECGs recorded in sinus rhythm. The algorithm calculates an atrial fibrillation risk score, classifying participants as high-risk (score ≥50) or low-risk (\<50). Monitoring strategies are adapted accordingly: high-risk participants undergo targeted and potentially repeated ECG monitoring using 12-lead ECG, Holter, or patch ECG, while low-risk participants follow standard guideline-based care.

Interventions

Usual Care (General Practice)

Participants receive routine care based on current clinical guidelines. Symptom-driven evaluation is performed by physicians, including at least one diagnostic test within 6 months such as a standard 12-lead ECG, Holter monitoring, or patch ECG. The choice and frequency of monitoring are determined by physician discretion, reflecting real-world practice patterns.

Intervention Type: OTHER

AI-ECG Guided Care (SmartECG-AFrisk)

Participants undergo SmartECG-AFrisk analysis of baseline 12-lead ECGs recorded in sinus rhythm. The algorithm calculates an atrial fibrillation risk score, classifying participants as high-risk (score ≥50) or low-risk (\<50). Monitoring strategies are adapted accordingly: high-risk participants undergo targeted and potentially repeated ECG monitoring using 12-lead ECG, Holter, or patch ECG, while low-risk participants follow standard guideline-based care.

Intervention Type: DEVICE

Eligibility Criteria

Inclusion Criteria

• 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 Criteria

• - Prior diagnosis of atrial fibrillation
• - Life expectancy ≤ 1 year

• Minimum Eligible Age: 30 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Yonsei University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Yonsei University College of Medicine

Seoul, , South Korea

Countries

South Korea

Central Contacts

Hee Tae Yu, MD

Role: CONTACT

heetyu@yuhs.ac

+82-2-2228-8460

Facility Contacts

Hee Tae yu, MD

Role: primary

heetyu@yuhs.ac

+82-2-2228-8460

Other Identifiers

1-2024-0069

Identifier Type: -

Identifier Source: org_study_id