Smartphone Based Digital Screening for Aortic Valve Stenosis
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Smartphone-based signal acquisition.
- Who it may be relevant to
- Registry conditions: Aortic Valve Stenosis, Artifical Intelligence. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Austria
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Overview
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.
Interventions
- Diagnostic test 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.
Primary outcome measures
- 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)]
Secondary outcome measures (4)
- 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]
Eligibility criteria
The following inclusion and exclusion criteria will be used for training, validation and test sets:
Inclusion criteria for group I (moderate to severe AS):
- 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
Inclusion criteria for group II:
- 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 (applicable for all groups):
- Informed consent form not signed.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Cohort
Study locations
Austria · 1 center
- Department of Internal Medicine III — Innsbruck
Identifiers
NCT: NCT07284550 · 1328/2020_1