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Artificial Intelligence in Aortic Regurgitation

NCT07486271 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 540

Last updated 2026-03-20

No results posted yet for this study

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.

Conditions

  • Aortic Regurgitation Disease

Interventions

DIAGNOSTIC_TEST

AI-Assisted Group

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

OTHER

Manual measurement group

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

Sponsors & Collaborators

  • Semmelweis University

    collaborator OTHER

  • The Prince Charles Hospital

    collaborator OTHER_GOV

  • Toho University

    collaborator OTHER

  • Us2.ai

    collaborator UNKNOWN

  • The University of New South Wales

    collaborator OTHER

  • Chinese University of Hong Kong

    lead OTHER

Principal Investigators

  • Alex PW Lee, Professor · Chinese University of Hong Kong

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
DOUBLE
Model
PARALLEL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-12-01
Primary Completion
2028-03-31
Completion
2028-03-31

Countries

  • Hong Kong

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07486271 on ClinicalTrials.gov