Aortic Valve StenosisSMART-VALVENCT07284550
Smartphone Based Digital Screening for Aortic Valve Stenosis
An observational study of Aortic Valve Stenosis and Artifical Intelligence, run by Medical University Innsbruck at 1 site in Austria.
Trial summary
Heart valve diseases are among the most serious cardiovascular conditions in older age. One of the most common forms is aortic valve stenosis, a narrowing of the valve opening between the left ventricle and the main artery. 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, Artifical Intelligence
- What is being tested?
- Smartphone-based signal acquisition
- How many people
- 500 participants
- Where
- 1 siteAustria
- Study dates
- 12 Jan 2026to Nov 2029
- 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: The SMART-VALVE project is a single-centre, proof-of-concept study. The study will be conducted in 2 stages. In stage 1, data will be collected to develop and validate an ML-based aortic stenosis classification algorithm. In stage 2, the developed algorithm is tested against newly acquired data from previously unseen participants. In stage 1, a total of 300 participants will be recruited for training and validation from clinical populations with moderate-to severe AS (group I) and a control group without significant Valvular Heart Disease (group II). Individuals in the control group will be matched to the AS patient group based on age, gender, and BMI (see Figure 5). The collected sensor data will be analysed to extract and engineer features and identify potential digital biomarkers indicative of aortic stenosis. AI algorithms will be applied to these datasets to develop predictive models for the classification of AS patients and individuals based on the recorded si
Who may be able to join
- Moderate to severe AS defined as AVA ≤ 1.5cm² in echocardiographic assessment
- No other significant VHD, valvular prosthesis, pacemaker or congenital heart defect
- Documented echocardiography as part of routine clinical practice no older than 90 days
- Patient age ≥ 18 years
- Provided written informed consent
- No significant VHD, valvular prosthesis, pacemaker or congenital heart defect
Show all 9Show fewer
- Documented echocardiography as part of routine clinical practice no older than 90 days
- Patient age ≥ 18 years
- Provided written informed consent
Who cannot join
- Informed consent form not signed.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Austria1 site, 1 recruiting
- Recruiting
Department of Internal Medicine III
Innsbruck, 6020
Michael Schreinlechner, MD, 004351250481308, Michael.Schreinlechner@i-med.ac.at
Österreich
About this study
Heart valve diseases are among the most serious cardiovascular conditions in older age. One of the most common forms is aortic valve stenosis, a narrowing of the valve opening between the left ventricle and the main artery. As the valve becomes tighter, the heart must work harder and harder to pump blood through the body. This process often develops slowly over many years and initially causes no clear symptoms. As a result, the condition is frequently detected only in advanced stages, when warning signs such as shortness of breath, chest pain, or dizziness appear. Without treatment, aortic valve stenosis can become life-threatening. If detected early, however, very effective treatment options are available today.
Up to now, the disease has been reliably diagnosed mainly through echocardiography. Yet this method is complex, costly, and requires specialized medical staff. A simple, affordable, and broadly accessible screening option does not yet exist.
Read more
The interdisciplinary clinical research project explores whether conventional smartphones could fill this gap. Almost all modern devices are equipped with sensors such as microphones, accelerometers, and gyroscopes. These can capture both heart sounds and subtle vibrations of the chest. The research team is investigating whether reliable diagnostic information for the diagnosis of aortic valve stenosis can be extracted from such recordings. To achieve this, the signals are processed with newly developed methods and analyzed using artificial intelligence.
For the study, several hundred patients with and without valve disease will be examined. The smartphone results will be compared with established diagnostic standards, particularly echocardiography, to test accuracy and reliability.
If successful, the approach could enable a straightforward, digital heart check at home using nothing more than a conventional smartphone. Such a tool would provide an accessible, low-cost, and widely available method for early detection, helping more people receive timely and potentially life-saving treatment.
- Conditions
- Aortic Valve Stenosis, Artifical Intelligence
- Being tested
- Smartphone-based signal acquisition
- Medical subject headings
- Aortic Valve Stenosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Smartphone-based signal acquisitionDiagnostic test
To enable the study, we have already developed a pipeline from smartphone-based signal acquisition to secure signal upload. This will be followed by analysis of the microphone, accelerometer and gyroscope data and development of algorithms based on to-be-defined signal features.
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity and specificity of a smartphone-derived algorithm for detecting moderate-to-severe aortic stenosis (AVA ≤ 1.5 cm²), using echocardiography as the reference standard
Sensitivity and specificity will be calculated by comparing the classification produced by the smartphone-based algorithm with the diagnosis obtained from transthoracic echocardiography, which serves as the clinical reference standard. Aortic stenosis severity will be defined according to established guideline criteria, with moderate-to-severe aortic stenosis classified as an aortic valve area (AVA) of ≤ 1.5 cm². Smartphone recordings will be obtained during a single study visit using built-in microphones and motion sensors to capture heart sounds and chest wall vibrations. Echocardiographic measurements, performed by certified clinical personnel, will provide the comparator classification. The reported outcome will reflect how accurately the smartphone algorithm identifies participants with moderate-to-severe aortic stenosis at this time point.
Measured: At the baseline study visit (after completion of smartphone and echocardiographic assessments)
Secondary outcomes (4)
Quality of smartphone-acquired cardiac signals, measured by signal-to-noise ratio (SNR)
Signal quality will be quantified by calculating the signal-to-noise ratio (SNR) of heart sound and vibration recordings captured using built-in smartphone microphones and motion sensors during the study visit. Higher SNR values indicate clearer cardiac signals with less background noise. The reported outcome reflects the feasibility and technical performance of the smartphone recording pipeline.
Measured: At the baseline study visit
Agreement between smartphone-derived aortic stenosis classification and echocardiographic grading, measured by Cohen's kappa coefficient
Agreement between the severity classification produced by the smartphone-based algorithm and the clinical reference standard (echocardiographic grading of aortic stenosis) will be quantified using Cohen's kappa coefficient. Echocardiographic classification will follow guideline-based severity thresholds. The reported value reflects the degree of concordance between both methods beyond chance.
Measured: At the baseline study visit
Area under the receiver operating characteristic curve (AUROC) of the smartphone-based algorithm for detecting moderate-to-severe aortic stenosis
The AUROC will be calculated to assess the discriminatory ability of the smartphone-based algorithm to distinguish between participants with and without moderate-to-severe aortic stenosis, as defined by an aortic valve area (AVA) ≤ 1.5 cm² on echocardiography. Higher AUROC values indicate better diagnostic performance.
Measured: At the baseline study visit
Incidence of major adverse cardiac and cerebrovascular events (MACCE)
Major adverse cardiac and cerebrovascular events (MACCE)-including all-cause mortality, cardiovascular mortality, non-fatal myocardial infarction, non-fatal stroke, and hospitalization for heart failure-will be recorded during follow-up. The clinical event data will be analyzed in relation to cardiac signal characteristics extracted from the baseline smartphone recordings (e.g., murmur intensity, dominant frequency patterns, signal-to-noise ratio). This outcome explores whether smartphone-derived cardiac features are associated with subsequent adverse clinical events.
Measured: Up to 12 months after the baseline study visit
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Dec 2025
- Study start12 Jan 2026
- Primary completionNov 2028 (estimated)
- Study completionNov 2029 (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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Medical University Innsbruck
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07284550
- Sponsor's protocol ID
- 1328/2020_1
- Acronym
- SMART-VALVE
- Status last verified
- May 2026
- First submitted
- 14 Nov 2025
- Last updated
- 28 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 28 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Smartphone Based Digital Screening for Aortic Valve Stenosis
- ClinicalTrials.gov ID
- NCT07284550
- Acronym / sponsor ID
- SMART-VALVE / 1328/2020_1
- Conditions
- Aortic Valve Stenosis, Artifical Intelligence
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Medical University InnsbruckUniversity, hospital or other
- Study start
- 12 Jan 2026 (actual)
- Primary completion
- Nov 2028 (estimated)
- Study completion
- Nov 2029 (estimated)
- First posted
- 16 Dec 2025 (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.
Heart valve diseases are among the most serious cardiovascular conditions in older age. One of the most common forms is aortic valve stenosis, a narrowing of the valve opening between the left ventricle and the main artery. As the valve becomes tighter, the heart must work harder and harder to pump blood through the body. This process often develops slowly over many years and initially causes no clear symptoms. As a result, the condition is frequently detected only in advanced stages, when warning signs such as shortness of breath, chest pain, or dizziness appear. Without treatment, aortic valve stenosis can become life-threatening. If detected early, however, very effective treatment options are available today.
Up to now, the disease has been reliably diagnosed mainly through echocardiography. Yet this method is complex, costly, and requires specialized medical staff. A simple, affordable, and broadly accessible screening option does not yet exist.
The interdisciplinary clinical research project explores whether conventional smartphones could fill this gap. Almost all modern devices are equipped with sensors such as microphones, accelerometers, and gyroscopes. These can capture both heart sounds and subtle vibrations of the chest. The research team is investigating whether reliable diagnostic information for the diagnosis of aortic valve stenosis can be extracted from such recordings. To achieve this, the signals are processed with newly developed methods and analyzed using artificial intelligence.
For the study, several hundred patients with and without valve disease will be examined. The smartphone results will be compared with established diagnostic standards, particularly echocardiography, to test accuracy and reliability.
If successful, the approach could enable a straightforward, digital heart check at home using nothing more than a conventional smartphone. Such a tool would provide an accessible, low-cost, and widely available method for early detection, helping more people receive timely and potentially life-saving treatment.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The SMART-VALVE project is a single-centre, proof-of-concept study. The study will be conducted in 2 stages. In stage 1, data will be collected to develop and validate an ML-based aortic stenosis classification algorithm. In stage 2, the developed algorithm is tested against newly acquired data from previously unseen participants. In stage 1, a total of 300 participants will be recruited for training and validation from clinical populations with moderate-to severe AS (group I) and a control group without significant Valvular Heart Disease (group II). Individuals in the control group will be matched to the AS patient group based on age, gender, and BMI (see Figure 5). The collected sensor data will be analysed to extract and engineer features and identify potential digital biomarkers indicative of aortic stenosis. AI algorithms will be applied to these datasets to develop predictive models for the classification of AS patients and individuals based on the recorded si
Key inclusion criteria (9 listed)
- Moderate to severe AS defined as AVA ≤ 1.5cm² in echocardiographic assessment
- No other significant VHD, valvular prosthesis, pacemaker or congenital heart defect
- Documented echocardiography as part of routine clinical practice no older than 90 days
- Patient age ≥ 18 years
- Provided written informed consent
Key exclusion criteria (1 listed)
- Informed consent form not signed.
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 |
|---|---|---|
| Smartphone-based signal acquisitionTo enable the study, we have already developed a pipeline from smartphone-based signal acquisition to secure signal upload. This will be followed by analysis of the microphone, accelerometer and gyroscope data and development of algorithms based on to-be-defined signal features. | Diagnostic test | – |
Primary outcome
Sensitivity and specificity of a smartphone-derived algorithm for detecting moderate-to-severe aortic stenosis (AVA ≤ 1.5 cm²), using echocardiography as the reference standard
Time frame: At the baseline study visit (after completion of smartphone and echocardiographic assessments)
Sensitivity and specificity will be calculated by comparing the classification produced by the smartphone-based algorithm with the diagnosis obtained from transthoracic echocardiography, which serves as the clinical reference standard. Aortic stenosis severity will be defined according to established guideline criteria, with moderate-to-severe aortic stenosis classified as an aortic valve area (AVA) of ≤ 1.5 cm². Smartphone recordings will be obtained during a single study visit using built-in microphones and motion sensors to capture heart sounds and chest wall vibrations. Echocardiographic measurements, performed by certified clinical personnel, will provide the comparator classification. The reported outcome will reflect how accurately the smartphone algorithm identifies participants with moderate-to-severe aortic stenosis at this time point.
4 secondary outcomes
Quality of smartphone-acquired cardiac signals, measured by signal-to-noise ratio (SNR)
Time frame: At the baseline study visit
Agreement between smartphone-derived aortic stenosis classification and echocardiographic grading, measured by Cohen's kappa coefficient
Time frame: At the baseline study visit
Area under the receiver operating characteristic curve (AUROC) of the smartphone-based algorithm for detecting moderate-to-severe aortic stenosis
Time frame: At the baseline study visit
Incidence of major adverse cardiac and cerebrovascular events (MACCE)
Time frame: Up to 12 months after the baseline study visit
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 28 May 2026.
- Individual participant dataSharing planned: Undecided
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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 Aortic Valve Stenosis and Artifical Intelligence, run by Medical University Innsbruck at 1 site in Austria.
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 9 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: looking for participants now, as of the sponsor's last update on 28 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 Austria. 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 Michael Schreinlechner, MD (+4351250425621, Michael.Schreinlechner@i-med.ac.at), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity and specificity of a smartphone-derived algorithm for detecting moderate-to-severe aortic stenosis (AVA ≤ 1.5 cm²), using echocardiography as the reference standard (time frame: At the baseline study visit (after completion of smartphone and echocardiographic assessments)). 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 28 May 2026.