Smartphone Based Digital Screening for Aortic Valve Stenosis

NCT ID: NCT07284550

Last Updated: 2025-12-16

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Total Enrollment: 500 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-12-31
• Study Completion Date: 2029-11-30

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

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Interventions

Smartphone-based signal acquisition

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.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• 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

Exclusion Criteria

• Informed consent form not signed.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Medical University Innsbruck

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Central Contacts

Michael Schreinlechner, MD

Role: CONTACT

Michael.Schreinlechner@i-med.ac.at

+4351250425621

Other Identifiers

1328/2020_1

Identifier Type: -

Identifier Source: org_study_id