Smartphone Based Digital Screening for Aortic Valve Stenosis

NCT07284550 | Not Yet Recruiting

• 1 other identifier: 1328/2020_1
• Study Type: observational
• Target: 500
• Locations: 0 countries
• Sites: N/A

Brief 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. 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.

Conditions

Aortic Valve Stenosis; Artifical Intelligence

Outcome Measures

Primary Outcomes (1)

• 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.

  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)

  At the baseline study visit

• Agreement between smartphone-derived aortic stenosis classification and echocardiographic grading, measured by Cohen's kappa coefficient

  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

  At the baseline study visit

• Incidence of major adverse cardiac and cerebrovascular events (MACCE)

  Up to 12 months after the baseline study visit

Interventions

Smartphone-based signal acquisition DIAGNOSTIC_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.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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

You may not qualify if:

• 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
• Documented echocardiography as part of routine clinical practice no older than 90 days
• Patient age ≥ 18 years
• Provided written informed consent
• Informed consent form not signed.

Sponsors & Collaborators

• Medical University Innsbruck: lead

MeSH Terms

Conditions

Aortic Valve Stenosis

Condition Hierarchy (Ancestors)

Aortic Valve Disease; Heart Valve Diseases; Heart Diseases; Cardiovascular Diseases; Ventricular Outflow Obstruction

Central Study Contacts

Michael Schreinlechner, MD

CONTACT

+4351250425621; Michael.Schreinlechner@i-med.ac.at

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 14, 2025
• First Posted: December 16, 2025
• Study Start: December 1, 2025
• Primary Completion (Estimated): November 1, 2028
• Study Completion (Estimated): November 1, 2029
• Last Updated: December 16, 2025

Record last verified: 2025-10