Heart Valve DiseasesNCT07568795
Clinical Validation of Portable Electronic Stethoscope for Detecting VHD
An observational study of Heart Valve Diseases, run by The University of Hong Kong at 1 site in Hong Kong.
Trial summary
The goal of this observational study is to evaluate accuracy of portable electronic stethoscope and machine learning-based diagnostic algorithms for detecting the disease in people with valvular heart disease and healthy controls. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Heart Valve Diseases
- What is being tested?
- BPM Core
- Type of treatment
- Medical device
- How many people
- 125 participants
- Where
- 1 siteHong Kong
- Study dates
- 3 Jul 2024to 31 Dec 2026
- Phase
- Observational
- 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
- Healthy volunteers accepted
- Adult, Older adult
Study population: 25 with aortic stenosis, 25 with mitral stenosis, 25 with mitral regurgitation, 25 with tricuspid regurgitation, and 25 without valvular heart disease
Who may be able to join
- Voluntarily agrees to participate by proving written informed consent
- Have echocardiography done within 5 years
Who cannot join
- Mechanical heart valve
- Adult congenital heart disease
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
Queen Mary Hospital
Hong Kong, Pok Fu Lam Rd.
22553838, qmh_enquiry@ha.org.hk
About this study
Official title: Clinical Validation of Portable Electronic Stethoscope for Detecting Valvular Heart Disease
The goal of this observational study is to evaluate accuracy of portable electronic stethoscope and machine learning-based diagnostic algorithms for detecting the disease in people with valvular heart disease and healthy controls. The main question it aims to answer is:
Is portable electronic stethoscope and machine learning-based diagnostic algorithms allow accurate detection of valvular heart disease?
Researchers will compare diagnostic algorithm's predictions with the clinicians' predictions to see if the diagnostic results are accurate.
Participants will
- take echocardiogram
- take electrocardiogram using BPM Core
- get the heart auscultation data measured via electronic stethoscope
Read the detailed description
The study compares the diagnostic accuracy of machine learning-based algorithms for diagnosis, which utilise auscultation data obtained through electronic stethoscopes, with the diagnoses made by clinicians using the same data. Two portable electronic stethoscopes used will be evaluated in this study, including BPM Core (Withings, France) and BeamO (Withings, France). The study utilises data collected from 100 patients at Queen Mary Hospital who have been diagnosed with valvular heart diseases (including aortic stenosis, mitral and/or tricuspid regurgitation, and mitral stenosis) and 25 healthy individuals without heart conditions.
- Conditions
- Heart Valve Diseases
- Being tested
- BPM Core
- Medical subject headings
- Heart Valve Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
BPM CoreDevice
Heart auscultation data will be collected from the patients in 5 different groups using BPM Core
| Study group | Role | What they receive |
|---|---|---|
| Aortic stenosis | Not described Receives: Device: BPM Core | |
| Mitral stenosis | Not described Receives: Device: BPM Core | |
| Mitral regurgitation | Not described Receives: Device: BPM Core | |
| Tricuspid regurgitation | Not described Receives: Device: BPM Core | |
| Control | Not described Receives: Device: BPM Core |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of valvular heart disease diagnosis using portable electronic stethoscope and machine learning-based diagnostic algorithms.
Comparison between the algorithm diagnosis to those made by the clinicians using the collected heart auscultation data and echocardiogram results
Measured: from admission to discharge, up to 1 hour
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 May 2026
- Study start3 Jul 2024
- Primary completion21 Aug 2024
- Study completion31 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Heart Valve DiseasesStudies of the same condition, open and under way.
- More trials run by The 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
- Observational
- Observational model
- Case-control
- Time perspective
- Prospective
- Enrolment
- 125 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The University of Hong Kong
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Wong Chun Ka, The University of Hong Kong
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07568795
- Sponsor's protocol ID
- PESVAH
- Status last verified
- Dec 2025
- First submitted
- 28 Apr 2026
- Last updated
- 6 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 6 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Clinical Validation of Portable Electronic Stethoscope for Detecting Valvular Heart Disease
- ClinicalTrials.gov ID
- NCT07568795
- Acronym / sponsor ID
- PESVAH
- Conditions
- Heart Valve Diseases
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 125 participants (estimated)
- Lead sponsor
- The University of Hong KongUniversity, hospital or other
- Study start
- 3 Jul 2024 (actual)
- Primary completion
- 21 Aug 2024 (actual)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 6 May 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.
The goal of this observational study is to evaluate accuracy of portable electronic stethoscope and machine learning-based diagnostic algorithms for detecting the disease in people with valvular heart disease and healthy controls. The main question it aims to answer is:
Is portable electronic stethoscope and machine learning-based diagnostic algorithms allow accurate detection of valvular heart disease?
Researchers will compare diagnostic algorithm's predictions with the clinicians' predictions to see if the diagnostic results are accurate.
Participants will
- take echocardiogram
- take electrocardiogram using BPM Core
- get the heart auscultation data measured via electronic stethoscope
Detailed description
The study compares the diagnostic accuracy of machine learning-based algorithms for diagnosis, which utilise auscultation data obtained through electronic stethoscopes, with the diagnoses made by clinicians using the same data. Two portable electronic stethoscopes used will be evaluated in this study, including BPM Core (Withings, France) and BeamO (Withings, France). The study utilises data collected from 100 patients at Queen Mary Hospital who have been diagnosed with valvular heart diseases (including aortic stenosis, mitral and/or tricuspid regurgitation, and mitral stenosis) and 25 healthy individuals without heart conditions.
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Number of arms
- 5
| Arm | Type | Receives |
|---|---|---|
| Aortic stenosis | – | Device: BPM Core |
| Mitral stenosis | – | Device: BPM Core |
| Mitral regurgitation | – | Device: BPM Core |
| Tricuspid regurgitation | – | Device: BPM Core |
| Control | – | Device: BPM Core |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- 25 with aortic stenosis, 25 with mitral stenosis, 25 with mitral regurgitation, 25 with tricuspid regurgitation, and 25 without valvular heart disease
Key inclusion criteria (2 listed)
- Voluntarily agrees to participate by proving written informed consent
- Have echocardiography done within 5 years
Key exclusion criteria (2 listed)
- Mechanical heart valve
- Adult congenital heart disease
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 |
|---|---|---|
| BPM CoreHeart auscultation data will be collected from the patients in 5 different groups using BPM Core | Device | Aortic stenosis, Control, Mitral regurgitation, Mitral stenosis, Tricuspid regurgitation |
Primary outcome
Accuracy of valvular heart disease diagnosis using portable electronic stethoscope and machine learning-based diagnostic algorithms.
Time frame: from admission to discharge, up to 1 hour
Comparison between the algorithm diagnosis to those made by the clinicians using the collected heart auscultation data and echocardiogram results
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 6 May 2026.
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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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Heart Valve Diseases, run by The 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 2 inclusion and 2 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 6 May 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 Chun Ka Wong, Clinical Assistant Professor (852 2255 3597, wongeck@hku.hk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of valvular heart disease diagnosis using portable electronic stethoscope and machine learning-based diagnostic algorithms. (time frame: from admission to discharge, up to 1 hour). 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 6 May 2026.