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NCT Number: NCT07023510

The VALVE-AI Trial

The goal of this clinical trial is to learn if an artificial intelligence-powered electrocardiogram (AI-ECG) can help detect moderate or severe valvular heart diseases (VHD) in adults. The main question it aims to answer is:

．Can AI-ECG screening identify patients with significant heart valve diseases who may benefit from early echocardiography? Researchers will compare the rate of moderate or severe VHD detection between the experimental group and the control group to see if AI-ECG improve the detection rate of significant VHD.

Participants will:

* Be classified as high- or low-risk for VHD using an AI-ECG system * In the experimental group, high-risk participants will receive echocardiography based on AI-ECG results * In the control group, usual clinical care will be provided without routine echocardiography for AI-ECG high-risk results.

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Key information

Conditions

Valvular Heart Disease Patients

Age range

60 year–85 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

About this study

This randomized controlled trial investigates the effectiveness of an artificial intelligence-powered electrocardiogram (AI-ECG) system for early screening of moderate or severe valvular heart disease (VHD) in adults receiving routine ECG examinations. The study population consists of adult outpatients undergoing a standard 12-lead ECG for any clinical indication. Each ECG is analyzed by a validated deep learning algorithm that automatically classifies the patient's risk for significant VHD.

Participants identified as high-risk by the AI-ECG system are randomized into either an experimental group or a control group. In the experimental group, high-risk participants undergo transthoracic echocardiography to confirm or exclude moderate or severe VHD. In the control group, high-risk participants continue with usual clinical care without additional echocardiographic screening based solely on the AI-ECG result. Low-risk participants in both groups receive routine care without additional intervention.

The primary aim is to determine whether AI-guided ECG screening, coupled with targeted echocardiography in the experimental group, increases the detection rate of clinically significant VHD compared to usual care. Secondary objectives include evaluating the impact on timely diagnosis, downstream clinical management, and the feasibility of integrating AI-ECG screening into routine outpatient workflows.

The study will follow participants for up to 90 days post-randomization to assess the detection rate and related outcomes.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • At least one 12-lead ECG within 1 year
  • Age 60-85 years of age

Exclusion criteria

  • Documented echocardiography within 3 years before indexed ECG
  • Any known valvular heart disease
  • History of any valvular surgery
  • Post-heart transplant

Treatment and study plan

AI-ECG driven echocardiography

Diagnostic Test

The intervention utilizes a previously validated deep learning model based on 12-lead electrocardiogram (ECG) data to screen for moderate-to-severe valvular heart diseases (VHD). The model processes raw ECG signals and integrates age and sex to enhance prediction. (doi: 10.18632/aging.205835.) Participants identified as high-risk for any moderate-to-severe VHD by the algorithm of artificial intelligence-powered electrocardiogram (AI-ECG) in this intervention arm will receive transthoracic echocardiography to confirm diagnosis and guide further management.

Primary outcomes

  1. Composite of Any Moderate or Severe VHD by Echocardiography

    Time frame: Within 90 days after randomization.

    The composite endpoint is defined as detecting any moderate or severe VHD by echocardiography, including mitral regurgitation (MR), aortic regurgitation (AR), aortic stenosis (AS), and tricuspid regurgitation (TR).

Secondary outcomes

  1. Number of Participants with Moderate or Severe MR by Echocardiography

    Time frame: Within 90 days after randomization.

  2. Number of Participants with Moderate or Severe AR by Echocardiography

    Time frame: Within 90 days after randomization.

  3. Number of Participants with Moderate or Severe AS by Echocardiography

    Time frame: Within 90 days after randomization.

  4. Number of Participants with Moderate or Severe TR by Echocardiography

    Time frame: Within 90 days after randomization.

  5. Number of Participants with Other Cardiac Diseases by Echocardiography

    Time frame: Within 90 days after randomization.

    The endpoint measures the number and proportion of atrial septal defect, ventricular septal defect, cardiac tamponade, and large pericardial effusion.

Study contacts

Contact information is provided by the study sponsor or research team.

Chin Lin

CONTACT

[email protected]

+886-2-8792-3100 ext. 18574

Yu-Lan Liu

CONTACT

[email protected]

+886-2-87923311 ext. 16118

Sponsors and collaborators

Lead sponsor

National Defense Medical Center, Taiwan

Other

Registry information

Official study title

VALidation of Screening Valvular Heart Disease Using Electrocardiogram Powered by Artificial Intelligence: A Randomized Controlled Trial

Important dates

Study start
2025
Primary completion
2026
Study completion
2026
First posted
Jun 17, 2025
Registry last updated
Jun 26, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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