Smartphone Based Digital Screening for Aortic Valve Stenosis

Condition(s) studied

Investigational drug(s) / intervention(s)

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.

Study 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.

Eligibility

Primary outcome measure(s)

• 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 — 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.

Trial sites (1)

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