Artificial Intelligence in Aortic Regurgitation

NCT07486271 | Recruiting DMC

• 1 other identifier: 2024.689-T
• Study Type: interventional
• Target: 540
• Locations: 1 country
• Sites: 1

Brief 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

Keywords

Aortic Regurgitation; Artificial Intelligence; Echocardiography; Automated Diagnosis; Clinical Efficacy

Outcome Measures

Primary Outcomes (1)

• Study Outcomes

  To compare the accuracy of the AI group and the manual group in distinguishing severe from non-severe AR, using expert cardiologists' (ASE level III or equivalent) assessments as the reference standard.

  This will be recorded from baseline to study completion (20 months)

Secondary Outcomes (8)

• Comparing Accuracy in Differentiating AR Severity Levels

  This will be recorded from baseline to study completion (20 months)

• Assessing deviations in Effective Regurgitant Orifice Area (EROA)

  This will be recorded from baseline to study completion (20 months)

• Assessing deviations in Vena Contracta (VC)

  This will be recorded from baseline to study completion (20 months)

• Assessing deviations in Proximal Isovelocity Surface Area (PISA)

  This will be recorded from baseline to study completion (20 months)

• Assessing deviations in jet width

  This will be recorded from baseline to study completion (20 months)

Study Arms (2)

AI-Assisted Group

ACTIVE COMPARATOR

AR severity will be assessed using an AI tool that evaluates grading and key echocardiographic parameters (e.g., EROA, VC, PISA, jet width, and RegVol), along with the time required for assessments.

Diagnostic Test: AI-Assisted Group

Manual Measurement Group

OTHER

AR severity will be assessed manually by trained sonographers following standard protocols. Cardiologists (ASE level III or equivalent), blinded to patient history and group assignment, will review both AI-generated and manual outputs to make final diagnoses and treatment decisions based solely on the initial assessments.

Other: Manual measurement group

Interventions

AI-Assisted Group DIAGNOSTIC_TEST

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

AI-Assisted Group

Manual measurement group OTHER

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

Manual Measurement Group

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Chinese University of Hong Kong: lead
• Semmelweis University: collaborator
• The Prince Charles Hospital: collaborator
• Toho University: collaborator
• Us2.ai: collaborator
• The University of New South Wales: collaborator

Study Sites (1)

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

Hong Kong, New Territories, Sha Tin, Hong Kong

RECRUITING

MeSH Terms

Conditions

Aortic Valve Insufficiency

Condition Hierarchy (Ancestors)

Aortic Valve Disease; Heart Valve Diseases; Heart Diseases; Cardiovascular Diseases

Study Officials

• Alex PW Lee, Professor

  Chinese University of Hong Kong

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Xueting Wang

CONTACT

(852) 3505 3840; xueting@cuhk.edu.hk

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: DOUBLE
• Who Masked: PARTICIPANT, INVESTIGATOR
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: March 12, 2026
• First Posted: March 20, 2026
• Study Start: December 1, 2025
• Primary Completion (Estimated): March 31, 2028
• Study Completion (Estimated): March 31, 2028
• Last Updated: March 20, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share

Locations

HK (1)