Aortic Regurgitation DiseaseNCT07486271
Artificial Intelligence in Aortic Regurgitation
A study testing AI-Assisted Group and Manual measurement group in people with Aortic Regurgitation Disease, run by Chinese University of Hong Kong at 1 site in Hong Kong.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Aortic Regurgitation Disease
- What is being tested?
- AI-Assisted Group and Manual measurement group
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 540 participants
- Where
- 1 siteHong Kong
- Study dates
- 1 Dec 2025to 31 Mar 2028
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Confirmed AR diagnosis via TTE and Doppler imaging per guidelines.
- Age ≥ 18 years.
- Adequate acoustic window for AR quantification.
Who cannot join
- Prior cardiac transplant or implanted cardiac devices.
- Poor image quality.
- Pregnancy or lactation.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Hong Kong1 site, 1 recruiting
- Recruiting
Division of Cardiology, Department of Medicine and Therapeutics Faculty of Medicine, The Chinese University of Hong Kong
Hong Kong, New Territories, Sha Tin
Xueting PW Wang, Professor, (852) 3505 3840, xueting@cuhk.edu.hk
About this study
Official title: Artificial Intelligence in Aortic Regurgitation: A Multicenter Randomised Controlled 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.
- Conditions
- Aortic Regurgitation Disease
- Being tested
- AI-Assisted Group, Manual measurement group
- Keywords
- Aortic Regurgitation, Artificial Intelligence, Echocardiography, Automated Diagnosis, Clinical Efficacy
- Medical subject headings
- Aortic Valve Insufficiency
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Assisted GroupDiagnostic test
Participants in this group will undergo aortic regurgitation assessment using an advanced artificial intelligence tool.
Manual measurement groupOther
Participants in this group will receive a traditional diagnostic assessment for aortic regurgitation, performed by trained sonographers following standard protocols.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. Receives: Other: Manual measurement group |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: This will be recorded from baseline to study completion (20 months)
Secondary outcomes (8)
Comparing Accuracy in Differentiating AR Severity Levels
To compare the accuracy of the AI group and the manual group in differentiating trace, mild, moderate, and severe aortic regurgitation, using cardiologists' assessments as the reference standard.
Measured: This will be recorded from baseline to study completion (20 months)
Assessing deviations in Effective Regurgitant Orifice Area (EROA)
The Effective Regurgitant Orifice Area (EROA) assesses the size of a valve opening that leads to backward blood flow in the heart. It is an important metric for evaluating valvular regurgitation and will be measured during echocardiography.
Measured: This will be recorded from baseline to study completion (20 months)
Assessing deviations in Vena Contracta (VC)
The Vena Contracta (VC) is an important measurement in echocardiography used to evaluate how severe mitral regurgitation is and will be measured during echocardiography.
Measured: This will be recorded from baseline to study completion (20 months)
Assessing deviations in Proximal Isovelocity Surface Area (PISA)
Proximal Isovelocity Surface Area (PISA) is a method used in echocardiography to evaluate mitral regurgitation and will be measured during echocardiography.
Measured: This will be recorded from baseline to study completion (20 months)
Assessing deviations in jet width
The jet width is a critical measurement used to assess the severity of aortic regurgitation and will be measured during echocardiography.
Measured: This will be recorded from baseline to study completion (20 months)
Assessing deviations in Regurgitant Volume (RegVol)
Regurgitant Volume assesses how much blood leaks back into the left atrium during mitral regurgitation and will be measured using Doppler echocardiography.
Measured: This will be recorded from baseline to study completion (20 months)
Comparing Assessment Completion Time
To compare the time taken by the AI group, the manual group, and the cardiologists to complete their assessments.
Measured: The time taken for each method to reach a diagnosis will be recorded from baseline to study completion (20 months)
Tracking 1-Year Outcomes
To track 1-year all-cause mortality and heart failure hospitalizations (HFH), comparing outcomes for patients with severe aortic regurgitation identified by the AI and manual groups, separately.
Measured: Participants will be followed up at 6 and 12 months to monitor outcomes, including 1-year all-cause mortality and HFH.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Mar 2026
- Study start1 Dec 2025
- Primary completion31 Mar 2028 (estimated)
- Study completion31 Mar 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Aortic Regurgitation DiseaseStudies of the same condition, open and under way.
- More trials run by Chinese University of Hong KongThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind
- Who is masked
- Participant, Investigator
- Enrolment
- 540 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Chinese University of Hong Kong
- Sponsor type
- University, hospital or other
- Collaborators
- Semmelweis University, The Prince Charles Hospital, Toho University, Us2.ai, The University of New South Wales
- Responsible party
- Principal investigator, Dr Alex PW Lee, Chinese University of Hong Kong
- Investigators
- Alex PW Lee, Professor, Principal investigator, Chinese University of Hong Kong
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07486271
- Sponsor's protocol ID
- 2024.689-T
- Status last verified
- Mar 2026
- First submitted
- 12 Mar 2026
- Last updated
- 20 Mar 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 20 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence in Aortic Regurgitation: A Multicenter Randomised Controlled Trial
- ClinicalTrials.gov ID
- NCT07486271
- Acronym / sponsor ID
- 2024.689-T
- Conditions
- Aortic Regurgitation Disease
- Keywords
- Aortic Regurgitation, Artificial Intelligence, Echocardiography, Automated Diagnosis, Clinical Efficacy
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 540 participants (estimated)
- Lead sponsor
- Chinese University of Hong KongUniversity, hospital or other
- Collaborators
- Semmelweis University, The Prince Charles Hospital, Toho University, Us2.ai, The University of New South Wales
- Study start
- 1 Dec 2025 (actual)
- Primary completion
- 31 Mar 2028 (estimated)
- Study completion
- 31 Mar 2028 (estimated)
- First posted
- 20 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind (participant, investigator)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI-Assisted Group | Active comparator | Diagnostic Test: AI-Assisted GroupAR 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. |
| Manual Measurement Group | Other | Other: Manual measurement groupAR 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- Confirmed AR diagnosis via TTE and Doppler imaging per guidelines.
- Age ≥ 18 years.
- Adequate acoustic window for AR quantification.
Key exclusion criteria (3 listed)
- Prior cardiac transplant or implanted cardiac devices.
- Poor image quality.
- Pregnancy or lactation.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-Assisted GroupParticipants in this group will undergo aortic regurgitation assessment using an advanced artificial intelligence tool. | Diagnostic test | AI-Assisted Group |
| Manual measurement groupParticipants in this group will receive a traditional diagnostic assessment for aortic regurgitation, performed by trained sonographers following standard protocols. | Other | Manual Measurement Group |
Primary outcome
Study Outcomes
Time frame: This will be recorded from baseline to study completion (20 months)
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.
8 secondary outcomes
Comparing Accuracy in Differentiating AR Severity Levels
Time frame: This will be recorded from baseline to study completion (20 months)
Assessing deviations in Effective Regurgitant Orifice Area (EROA)
Time frame: This will be recorded from baseline to study completion (20 months)
Assessing deviations in Vena Contracta (VC)
Time frame: This will be recorded from baseline to study completion (20 months)
Assessing deviations in Proximal Isovelocity Surface Area (PISA)
Time frame: This will be recorded from baseline to study completion (20 months)
Assessing deviations in jet width
Time frame: This will be recorded from baseline to study completion (20 months)
Assessing deviations in Regurgitant Volume (RegVol)
Time frame: This will be recorded from baseline to study completion (20 months)
Comparing Assessment Completion Time
Time frame: The time taken for each method to reach a diagnosis will be recorded from baseline to study completion (20 months)
Tracking 1-Year Outcomes
Time frame: Participants will be followed up at 6 and 12 months to monitor outcomes, including 1-year all-cause mortality and HFH.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Mar 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Alex PW Lee, Professor principal investigator
Chinese University of Hong Kong
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI-Assisted Group and Manual measurement group in people with Aortic Regurgitation Disease, run by Chinese University of Hong Kong at 1 site in Hong Kong.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 20 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Hong Kong. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Xueting Wang ((852) 3505 3840, xueting@cuhk.edu.hk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Study Outcomes (time frame: This will be recorded from baseline to study completion (20 months)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Mar 2026.