Portable electronic stethoscope and machine-learning detection of heart valve disease
Clinical Validation of Portable Electronic Stethoscope for Detecting Valvular Heart Disease
We will test whether a portable electronic stethoscope combined with a machine-learning algorithm can accurately detect valvular heart disease in people with known valve problems and in healthy volunteers.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 125 (estimated) |
| Ages | 18 Years and up |
| Sex | All |
| Sponsor | The University of Hong Kong Academic / other |
| Locations | 1 site (Hong Kong, Pok Fu Lam Rd.) |
| Trial ID | NCT07568795 on ClinicalTrials.gov |
What this trial studies
This observational study records heart sounds with two portable electronic stethoscopes (BPM Core and BeamO) and compares machine-learning algorithm predictions to clinicians' interpretations and echocardiogram results. Participants include 100 patients diagnosed with valvular heart disease and 25 healthy controls recruited at Queen Mary Hospital in Hong Kong. Each participant will undergo echocardiography (within the prior five years), an ECG using the BPM Core device, and auscultation recordings for algorithm analysis. The main outcome is diagnostic agreement and accuracy of the algorithms versus clinician readings and echocardiographic findings.
Who should consider this trial
Good fit: Adults who can give informed consent, have a prior echocardiogram within five years, and either have a diagnosed valvular heart condition or are healthy volunteers willing to attend Queen Mary Hospital are ideal candidates.
Not a fit: People with mechanical heart valves or adult congenital heart disease are excluded and are unlikely to benefit from this specific algorithm-based detection approach.
Why it matters
Potential benefit: If successful, this approach could provide a low-cost, portable screening tool to help detect valvular heart disease earlier and expand access to screening outside specialist centers.
How similar studies have performed: Prior research using electronic auscultation and machine-learning has shown promising but variable accuracy for detecting murmurs and valve lesions, with external validation still limited.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * Voluntarily agrees to participate by proving written informed consent * Have echocardiography done within 5 years Exclusion Criteria: * Mechanical heart valve * Adult congenital heart disease
Where this trial is running
Hong Kong, Pok Fu Lam Rd.
- Queen Mary Hospital — Hong Kong, Pok Fu Lam Rd., Hong Kong (Recruiting)
Study contacts
- Study coordinator: Chun Ka Wong, Clinical Assistant Professor
- Email: wongeck@hku.hk
- Phone: 852 2255 3597
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.