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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:
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| AI-ECG | Experimental | Participants whose electrocardiogram is classified as high-risk for moderate or severe valvular heart disease (VHD) by the artificial intelligence-powered electrocardiogram (AI-ECG) system will receive additional transthoracic echocardiography, regardless of whether the treating physician suspects VHD based on symptoms or physical examination. Low-risk participants continue with routine care without additional intervention. |
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| Usual care | No Intervention | Participants whose electrocardiogram is classified as high-risk for moderate or severe valvular heart diseases (VHD) by the artificial intelligence-powered electrocardiogram (AI-ECG) system receive standard care according to routine clinical practice. Transthoracic echocardiography is arranged only if the treating physician deems it clinically necessary based on the symptoms, physical examination, , or other non-AI findings. Low-risk participants continue with routine care without additional intervention. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Composite of Any Moderate or Severe VHD by Echocardiography | 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). | Within 90 days after randomization. |
| Measure | Description | Time Frame |
|---|---|---|
| Number of Participants with Moderate or Severe MR by Echocardiography | Within 90 days after randomization. | |
| Number of Participants with Moderate or Severe AR by Echocardiography | Within 90 days after randomization. |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Chin Lin | Contact | +886-2-8792-3100 | 18574 | xup6fup0629@gmail.com |
| Yu-Lan Liu | Contact | +886-2-87923311 | 16118 | cylgist80131@gmail.com |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Tri-Service General Hospital | Recruiting | Taipei | Taiwan |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 38761181 | Result | Lin YT, Lin CS, Tsai CS, Tsai DJ, Lou YS, Fang WH, Lee YT, Lin C. Comprehensive clinical application analysis of artificial intelligence-enabled electrocardiograms for screening multiple valvular heart diseases. Aging (Albany NY). 2024 May 16;16(10):8717-8731. doi: 10.18632/aging.205835. Epub 2024 May 16. |
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| Number of Participants with Moderate or Severe AS by Echocardiography | Within 90 days after randomization. |
| Number of Participants with Moderate or Severe TR by Echocardiography | Within 90 days after randomization. |
| Number of Participants with Other Cardiac Diseases by Echocardiography | The endpoint measures the number and proportion of atrial septal defect, ventricular septal defect, cardiac tamponade, and large pericardial effusion. | Within 90 days after randomization. |