Clinical trial NCT06404437: Detection of Aortic Stenosis With Smartphone Auscultation Using Machine Learning (HEARTBEAT-Pilot)
Official title: Detection of Aortic Stenosis With Smartphone Auscultation Using Machine Learning (HEARTBEAT-Pilot)
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Aortic Valve Stenosis
Keywords: Machine Learning
Digital Health
Heart Valve Disease
Cardiology
Lead sponsor: Friedrich-Alexander-Universität Erlangen-Nürnberg
Sponsor class: OTHER
Collaborators: name: University of Erlangen-Nürnberg Medical School
First posted: 2024-05-08
Enrollment: 100
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: No relevant heart valve disease or severe aortic stenosis with no other relevant heart valve disease

Brief summary: Severe aortic stenosis, a common heart valve issue, is usually treated surgically or through intervention. Diagnosis typically occurs after symptoms appear, but research suggests already treating asymptomatic cases may help patients live longer. Current diagnostics using echocardiography are detailed but time-consuming, prompting the exploration of a smartphone application using built-in microphones and machine learning for quicker and more accessible screening.

Detailed description: Severe aortic stenoses usually is treated either surgically or interventionally, making it the most frequently treated among heart valve diseases. Typically, severe aortic stenosis is diagnosed only after the onset of the first symptoms. However, initial studies suggest that treating asymptomatic aortic stenoses could also extend the lifespan of affected individuals. Therefore, a widely applicable and cost-effective diagnostic method would be desirable for screening.

The current gold standard for diagnosing aortic stenosis is echocardiography. It allows for detailed measurement and evaluation, assisting in detection and diagnostic assessment. However, it is time-consuming and therefore not readily applicable to a larger population. Alternatively, auscultation as an acoustic method is suitable, where typical noise changes due to turbulence in blood flow can be detected using a stethoscope.

Since stethoscopes are only conditionally accessible for self-use, both in terms of availability and usability, this study aims to investigate whether a mobile application based on artificial intelligence for common smartphones using built-in microphones can also be diagnostically used. For this purpose, microphone recordings at the typical five auscultation points of 50 patients with severe aortic stenosis and 50 patients without any relevant heart valve disease are recorded. A digital stethoscope (3M Deutschland GmbH, Germany) and echocardiography findings serve as references. Based on the data, a classification model will be developed in a first step, which can detect severe aortic stenoses in smartphone recordings using machine learning.

Eligibility criteria: Inclusion Criteria:

* Age ≥ 18 years
* No relevant heart valve disease or severe aortic stenosis with no other relevant heart valve disease in echocardiography no older than 3 months

Exclusion Criteria:

* Previous surgerical or interventional therapy of a heart valve

Primary outcomes:
measure: Algorithm Performance
timeFrame: Baseline
Secondary outcomes:
measure: Comparison with Digital Stethoscope
timeFrame: Baseline
measure: Comparison of Auscultation Points
timeFrame: Baseline
Other outcomes:
Locations:
city: Erlangen
country: Germany
facility: Deparment of Medicine 2 - Cardiology and Angiology, Friedrich-Alexander-Universität Erlangen-Nürnberg
Study officials:
References: