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.

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.

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