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ClinicalTrials.gov ID

NCT07486739

Lead Sponsor

Seoul National University HospitalOTHER

Overall Status

Not yet recruiting

Study Type

Interventional

Phase

Not Applicable

Enrollment

1,724Estimated

Last Update Posted

Jul 23, 2026Actual

Start Date

Jul 29, 2026Estimated

Primary Completion Date

Dec 31, 2030Estimated

Completion Date

Dec 31, 2030Estimated

Official Title

Functional And STructural Assesment of the Heart by Artificial Intelligence-enabled Electrocardiogram for the Management of Atrial Fibrillation

Brief Summary

The objective of this study is to evaluate whether an AI-ECG based screening strategy for detecting cardiac functional and structural abnormalities preserves clinical effectiveness and safety, compared with a conventional strategy of routine echocardiography in patients with AF, thereby demonstrating the non-inferiority of AI-ECG guided care.

Detailed Description

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, with its prevalence having more than doubled over the past decade. AF is associated with an increased risk of stroke, heart failure, and mortality, thereby imposing a substantial burden on both patients and healthcare systems. Accordingly, contemporary clinical guidelines emphasize accurate diagnosis and early, integrated management of AF. In this context, transthoracic echocardiography has become a standard diagnostic tool for the assessment of structural heart disease and cardiac function.

Despite being non-invasive and relatively low-cost, echocardiography is subject to several system-level limitations in routine clinical practice, including dependence on specialized equipment and trained personnel, scheduling delays, and inefficiencies related to repeated examinations. These constraints may create bottlenecks in the timely initiation and optimization of AF management.

In real-world practice, a considerable proportion of patients with AF undergo echocardiography primarily to confirm the absence of significant structural heart disease or impaired function. A uniform strategy of performing echocardiography in all patients with AF may not be optimal from the perspectives of patient convenience and healthcare resource utilization. Moreover, depending on healthcare system capacity, access to echocardiography may delay the timely selection of optimal AF management. Conversely, selectively performing echocardiography in patients with a higher likelihood of structural or functional cardiac abnormalities may allow for a more efficient, timely, and targeted diagnostic approach.

Artificial intelligence-enabled electrocardiography (AI-ECG) offers several practical advantages, including very short acquisition time, patients' convenience, substantially lower cost, and feasibility for repeated assessments during follow-up. AI-ECG may enable sensitive detection of changes in a patient's cardiac status over time. Positioning AI-ECG as an initial screening tool to identify patients with suspected structural or functional heart disease could facilitate a "screening-confirmation" diagnostic pathway, in which echocardiography is reserved for patients with abnormal or suspicious findings on AI-ECG. Such an approach has the potential to streamline initial and follow-up evaluations while maintaining patient safety.

• Atrial Fibrillation (AF)

• Artificial intelligence-enabled electrocardiography
• Transthoracic Echocardiography-Guided Assessment

Ages Eligible for Study

Adult (18-64)Older Adult (65+)

Sexes Eligible for Study

All

Accepts Healthy Volunteers

No

Eligibility Criteria

Inclusion Criteria:

1. AF documented by electrocardiography within the past 12 months

   • AF documented on a 12-lead electrocardiogram or recorded for ≥30 seconds on a single-lead or multi-lead electrocardiogram.

2. Patients for whom an initial or repeat transthoracic echocardiographic evaluation is clinically indicated.

3. A CHA₂DS₂-VA score of ≥2.

4. Aged ≥19 years at the time of enrollment and able to provide written informed consent voluntarily.

Exclusion Criteria:

1. Transthoracic echocardiography performed within the past 6 months.
2. Ventricular rate ≥110 beats per minute during atrial fibrillation.
3. Atrial fibrillation due to a reversible cause.
4. New York Heart Association (NYHA) functional class IV or European Heart Rhythm Association (EHRA) class IV symptoms.
5. Known history of structural heart disease or clinical findings suggestive of structural heart disease based on medical history and physical examination. (e.g., presence of a cardiac murmur of Levine scale grade 3 or higher on auscultation, or murmurs suggestive of moderate to severe mitral stenosis, such as an opening snap or diastolic rumbling murmur).
6. Baseline electrocardiographic conduction abnormalities or significant electrocardiographic findings suggestive of clinically meaningful structural heart disease (e.g., Mobitz type II second-degree atrioventricular block, third-degree atrioventricular block, or QTc ≥480 ms).
7. History of prior cardiac surgery.
8. History of acute coronary syndrome or coronary revascularization within the past 90 days.
9. History of intracardiac thrombosis or systemic thromboembolism within the past 90 days.
10. History of transient ischemic attack, ischemic stroke, or intracranial hemorrhage within the past 90 days.
11. History of ventricular tachycardia or ventricular fibrillation.
12. Severe liver disease associated with coagulopathy (e.g., AST or ALT >3× the upper limit of normal, or total bilirubin >2× the upper limit of normal).
13. Severe chronic kidney disease (stage V), requiring or imminently requiring dialysis.
14. Contraindication to anticoagulation therapy.
15. Pregnancy, breastfeeding, or planning pregnancy during the study period.
16. Life expectancy of less than 1 year.
17. Current participation in another randomized clinical trial.

Primary Purpose

Treatment

Allocation

Randomized

Interventional Model

Parallel Assignment

Masking

None (Open Label)

Lead Sponsor

Seoul National University HospitalOTHER

Collaborators

Medical AIINDUSTRY

medhan69@hallym.or.kr

thinkmed@naver.com

tgfbk@naver.com

kdongmin@yahoo.com

mdleeys@cu.ac.kr

drparkgyver@gmail.com

nsaids77@daum.net

ypruimin@gilhospital.com

youngjoin@naver.com

atropin5@cha.ac.kr

d070294@gmail.com

woosion@gmail.com

ferryst@naver.com

lexie6169@gmail.com

insooya1203@yuhs.ac

cardiohy@hanyang.ac.kr

minpupets01.kim@samsung.com

managingheart@gmail.com

esjin@khu.ac.kr

jinbbai@khu.ac.kr

yk.on@samsung.com

choiek417@gmail.com

kimdhoon@yuhs.ac

sneeze_kr@naver.com