Aortic Valve StenosisNCT06404437
Detection of Aortic Stenosis With Smartphone Auscultation Using Machine Learning (HEARTBEAT-Pilot)
An observational study of Aortic Valve Stenosis, run by Friedrich-Alexander-Universität Erlangen-Nürnberg at 1 site in Germany.
Trial summary
Severe aortic stenosis, a common heart valve issue, is usually treated surgically or through intervention. Diagnosis typically occurs after symptoms appear, but research suggests already treating asymptomatic cases may help patients live longer. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Aortic Valve Stenosis
- What is being tested?
- Auscultation
- How many people
- 100 participants
- Where
- 1 siteGermany
- Study dates
- 9 Mar 2023to 1 Mar 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: No relevant heart valve disease or severe aortic stenosis with no other relevant heart valve disease
Who may be able to join
- Age ≥ 18 years
- No relevant heart valve disease or severe aortic stenosis with no other relevant heart valve disease in echocardiography no older than 3 months
Who cannot join
- Previous surgerical or interventional therapy of a heart valve
Trial locations and contacts
1 site in 1 country.
Germany1 site
Deparment of Medicine 2 - Cardiology and Angiology, Friedrich-Alexander-Universität Erlangen-Nürnberg
Erlangen, 91054
About this study
Severe aortic stenosis, a common heart valve issue, is usually treated surgically or through intervention. Diagnosis typically occurs after symptoms appear, but research suggests already treating asymptomatic cases may help patients live longer. Current diagnostics using echocardiography are detailed but time-consuming, prompting the exploration of a smartphone application using built-in microphones and machine learning for quicker and more accessible screening.
Read the detailed description
Severe aortic stenoses usually is treated either surgically or interventionally, making it the most frequently treated among heart valve diseases. Typically, severe aortic stenosis is diagnosed only after the onset of the first symptoms. However, initial studies suggest that treating asymptomatic aortic stenoses could also extend the lifespan of affected individuals. Therefore, a widely applicable and cost-effective diagnostic method would be desirable for screening.
The current gold standard for diagnosing aortic stenosis is echocardiography. It allows for detailed measurement and evaluation, assisting in detection and diagnostic assessment. However, it is time-consuming and therefore not readily applicable to a larger population. Alternatively, auscultation as an acoustic method is suitable, where typical noise changes due to turbulence in blood flow can be detected using a stethoscope.
Since stethoscopes are only conditionally accessible for self-use, both in terms of availability and usability, this study aims to investigate whether a mobile application based on artificial intelligence for common smartphones using built-in microphones can also be diagnostically used. For this purpose, microphone recordings at the typical five auscultation points of 50 patients with severe aortic stenosis and 50 patients without any relevant heart valve disease are recorded. A digital stethoscope (3M Deutschland GmbH, Germany) and echocardiography findings serve as references. Based on the data, a classification model will be developed in a first step, which can detect severe aortic stenoses in smartphone recordings using machine learning.
- Conditions
- Aortic Valve Stenosis
- Being tested
- Auscultation
- Keywords
- Machine Learning, Digital Health, Heart Valve Disease, Cardiology
- Medical subject headings
- Aortic Valve Stenosis, Heart Valve Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AuscultationDiagnostic test
Auscultation at five auscultation points using a digital stethoscope and a smartphone
| Study group | Role | What they receive |
|---|---|---|
| Severe Aortic Stenosis | Not described Receives: Diagnostic Test: Auscultation | |
| No Relevant Heart Valve Disease | Not described Receives: Diagnostic Test: Auscultation |
What is being measured
The primary outcome is the main question the study is designed to answer.
Algorithm Performance
Performance of algorithmic diagnosis measured by accuracy, sensitivity, specificity, and positive predictive value
Measured: Baseline
Secondary outcomes (2)
Comparison with Digital Stethoscope
Comparison of algorithm performance using smartphone recordings with algorithm performance using digital stethoscope recordings
Measured: Baseline
Comparison of Auscultation Points
Comparison of algorithm performance using different sets of auscultation points
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 May 2024
- Study start9 Mar 2023
- Primary completion22 May 2024
- Study completion1 Mar 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Aortic Valve StenosisStudies of the same condition, open and under way.
- 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
- Other
- Time perspective
- Prospective
- Enrolment
- 100 participants (actual)
Sponsor and investigators
- Lead sponsor
- Friedrich-Alexander-Universität Erlangen-Nürnberg
- Sponsor type
- University, hospital or other
- Collaborators
- University of Erlangen-Nürnberg Medical School, University Hospital Erlangen
- Responsible party
- Principal investigator, Johannes Michael Altstidl, Friedrich-Alexander-Universität Erlangen-Nürnberg
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06404437
- Sponsor's protocol ID
- 23-39-B
- Status last verified
- Dec 2025
- First submitted
- 5 May 2024
- Last updated
- 24 Dec 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Recordings and metadata are published
Study at a glance
From the registry record, last updated by the sponsor on 24 Dec 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Detection of Aortic Stenosis With Smartphone Auscultation Using Machine Learning (HEARTBEAT-Pilot)
- ClinicalTrials.gov ID
- NCT06404437
- Acronym / sponsor ID
- 23-39-B
- Conditions
- Aortic Valve Stenosis
- Keywords
- Machine Learning, Digital Health, Heart Valve Disease, Cardiology
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 100 participants (actual)
- Lead sponsor
- Friedrich-Alexander-Universität Erlangen-NürnbergUniversity, hospital or other
- Collaborators
- University of Erlangen-Nürnberg Medical School, University Hospital Erlangen
- Study start
- 9 Mar 2023 (actual)
- Primary completion
- 22 May 2024 (actual)
- Study completion
- 1 Mar 2026 (estimated)
- First posted
- 8 May 2024 (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.
Severe aortic stenosis, a common heart valve issue, is usually treated surgically or through intervention. Diagnosis typically occurs after symptoms appear, but research suggests already treating asymptomatic cases may help patients live longer. Current diagnostics using echocardiography are detailed but time-consuming, prompting the exploration of a smartphone application using built-in microphones and machine learning for quicker and more accessible screening.
Detailed description
Severe aortic stenoses usually is treated either surgically or interventionally, making it the most frequently treated among heart valve diseases. Typically, severe aortic stenosis is diagnosed only after the onset of the first symptoms. However, initial studies suggest that treating asymptomatic aortic stenoses could also extend the lifespan of affected individuals. Therefore, a widely applicable and cost-effective diagnostic method would be desirable for screening.
The current gold standard for diagnosing aortic stenosis is echocardiography. It allows for detailed measurement and evaluation, assisting in detection and diagnostic assessment. However, it is time-consuming and therefore not readily applicable to a larger population. Alternatively, auscultation as an acoustic method is suitable, where typical noise changes due to turbulence in blood flow can be detected using a stethoscope.
Since stethoscopes are only conditionally accessible for self-use, both in terms of availability and usability, this study aims to investigate whether a mobile application based on artificial intelligence for common smartphones using built-in microphones can also be diagnostically used. For this purpose, microphone recordings at the typical five auscultation points of 50 patients with severe aortic stenosis and 50 patients without any relevant heart valve disease are recorded. A digital stethoscope (3M Deutschland GmbH, Germany) and echocardiography findings serve as references. Based on the data, a classification model will be developed in a first step, which can detect severe aortic stenoses in smartphone recordings using machine learning.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Severe Aortic Stenosis | – | Diagnostic Test: Auscultation |
| No Relevant Heart Valve Disease | – | Diagnostic Test: Auscultation |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- No relevant heart valve disease or severe aortic stenosis with no other relevant heart valve disease
Key inclusion criteria (2 listed)
- Age ≥ 18 years
- No relevant heart valve disease or severe aortic stenosis with no other relevant heart valve disease in echocardiography no older than 3 months
Key exclusion criteria (1 listed)
- Previous surgerical or interventional therapy of a heart valve
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 |
|---|---|---|
| AuscultationAuscultation at five auscultation points using a digital stethoscope and a smartphone | Diagnostic test | No Relevant Heart Valve Disease, Severe Aortic Stenosis |
Primary outcome
Algorithm Performance
Time frame: Baseline
Performance of algorithmic diagnosis measured by accuracy, sensitivity, specificity, and positive predictive value
2 secondary outcomes
Comparison with Digital Stethoscope
Time frame: Baseline
Comparison of Auscultation Points
Time frame: Baseline
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 24 Dec 2025.
- Individual participant dataSharing planned: Yes. Recordings and metadata are published
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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. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
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 Aortic Valve Stenosis, run by Friedrich-Alexander-Universität Erlangen-Nürnberg at 1 site in Germany.
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 1 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: under way, not looking for participants, as of the sponsor's last update on 24 Dec 2025. 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 Germany. 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?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Algorithm Performance (time frame: Baseline). 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 24 Dec 2025.