AI tool to detect and measure aortic regurgitation
Artificial Intelligence in Aortic Regurgitation: A Multicenter Randomised Controlled Trial
NA · Chinese University of Hong Kong · NCT07486271
This project will test an AI-assisted echocardiography tool to detect and measure aortic regurgitation in adults and compare its results with standard manual measurements.
Quick facts
| Phase | NA |
|---|---|
| Study type | Interventional |
| Enrollment | 540 (estimated) |
| Ages | 18 Years and up |
| Sex | All |
| Sponsor | Chinese University of Hong Kong (other) |
| Locations | 1 site (Hong Kong, New Territories) |
| Trial ID | NCT07486271 on ClinicalTrials.gov |
What this trial studies
Deep learning algorithms will be trained to analyze echocardiographic images and integrate multiple Doppler and structural parameters to automatically detect and quantify aortic regurgitation. The AI system will be validated in a multicenter randomized controlled trial that assigns patients to AI-assisted interpretation or conventional manual measurement and compares diagnostic accuracy and consistency. Participating centers will use standardized image acquisition protocols with central adjudication to ensure comparability across sites. The approach aims to reduce inter-operator variability, speed diagnosis, and make advanced AR assessment more accessible, including in resource-limited settings.
Who should consider this trial
Good fit: Adults (age 18 and older) with a confirmed aortic regurgitation diagnosis on transthoracic echocardiography and an adequate acoustic window are ideal candidates.
Not a fit: Patients with poor image quality, prior cardiac transplant or implanted cardiac devices, or who are pregnant or breastfeeding are unlikely to benefit or be eligible.
Why it matters
Potential benefit: If successful, the AI tool could provide faster, more consistent AR diagnoses and extend specialist-level echo interpretation to more hospitals.
How similar studies have performed: Previous work has shown promising accuracy for AI in echocardiography and valvular disease, but large multicenter randomized comparisons specifically focused on AR quantification are still limited.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * Confirmed AR diagnosis via TTE and Doppler imaging per guidelines. * Age ≥ 18 years. * Adequate acoustic window for AR quantification. Exclusion Criteria: * Prior cardiac transplant or implanted cardiac devices. * Poor image quality. * Pregnancy or lactation.
Where this trial is running
Hong Kong, New Territories
- Division of Cardiology, Department of Medicine and Therapeutics Faculty of Medicine, The Chinese University of Hong Kong — Hong Kong, New Territories, Hong Kong (RECRUITING)
Study contacts
- Principal investigator: Alex PW Lee, Professor — Chinese University of Hong Kong
- Study coordinator: Xueting Wang
- Email: xueting@cuhk.edu.hk
- Phone: (852) 3505 3840
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.
Conditions: Aortic Regurgitation Disease, Aortic Regurgitation, Artificial Intelligence, Echocardiography, Automated Diagnosis, Clinical Efficacy