About this trial
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 criteria
Qualifiers
Confirmed AR diagnosis via TTE and Doppler imaging per guidelines.
Age ≥ 18 years.
Adequate acoustic window for AR quantification.
Disqualifiers
Prior cardiac transplant or implanted cardiac devices.
Poor image quality.
Pregnancy or lactation.
Trial design
Treatments tested in this trial
- AI-Assisted Group
- Manual measurement group
Treatment groups
Sponsors and collaborators
Chinese University of Hong Kong
Lead sponsor
Semmelweis University
Collaborator
The Prince Charles Hospital
Collaborator
Toho University
Collaborator
Us2.ai
Collaborator
The University of New South Wales
Collaborator