RecruitingNot ApplicableNCT07486271

Artificial Intelligence in Aortic Regurgitation

Artificial Intelligence in Aortic Regurgitation: A Multicenter Randomised Controlled Trial


Sponsor

Chinese University of Hong Kong

Enrollment

540 participants

Start Date

Dec 1, 2025

Study Type

INTERVENTIONAL

Conditions

Summary

This research project aims to develop and validate a tool that uses artificial intelligence (AI) to automatically detect and quantify aortic regurgitation (AR). The clinical efficacy of this tool will be established by comparing it to manual diagnostic methods in a multicenter randomized controlled trial. By leveraging deep learning (DL) techniques, the AI system will automate aortic regurgitation (AR) detection, measurement, and diagnosis, addressing challenges like variability in echocardiographic interpretations and the need for specialized expertise. It will integrate multiple echocardiographic parameters to provide accurate, standardized, and efficient AR diagnoses, reducing human error and improving consistency. This tool will enhance diagnostic precision and accessibility, improving clinical outcomes and extending advanced diagnostic capabilities to a broader range of healthcare environments, including resource-limited settings.


Eligibility

Min Age: 18 Years

Inclusion Criteria3

  • Confirmed AR diagnosis via TTE and Doppler imaging per guidelines.
  • Age ≥ 18 years.
  • Adequate acoustic window for AR quantification.

Exclusion Criteria3

  • Prior cardiac transplant or implanted cardiac devices.
  • Poor image quality.
  • Pregnancy or lactation.

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Interventions

DIAGNOSTIC_TESTAI-Assisted Group

Participants in this group will undergo aortic regurgitation assessment using an advanced artificial intelligence tool.

OTHERManual measurement group

Participants in this group will receive a traditional diagnostic assessment for aortic regurgitation, performed by trained sonographers following standard protocols.


Locations(1)

Division of Cardiology, Department of Medicine and Therapeutics Faculty of Medicine, The Chinese University of Hong Kong

Hong Kong, New Territories, Hong Kong

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NCT07486271


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