MedDataX Logo

Detection of Aortic Stenosis With Smartphone Auscultation Using Machine Learning (HEARTBEAT-Pilot)

NCT ID: NCT06404437

Last Updated: 2025-12-24

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

ACTIVE_NOT_RECRUITING

Total Enrollment

100 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-03-09

Study Completion Date

2026-03-01

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

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.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Aortic Valve Stenosis

Keywords

Explore important study keywords that can help with search, categorization, and topic discovery.

Machine Learning Digital Health Heart Valve Disease Cardiology

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

OTHER

Study Time Perspective

PROSPECTIVE

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

Severe Aortic Stenosis

Auscultation

Intervention Type DIAGNOSTIC_TEST

Auscultation at five auscultation points using a digital stethoscope and a smartphone

No Relevant Heart Valve Disease

Auscultation

Intervention Type DIAGNOSTIC_TEST

Auscultation at five auscultation points using a digital stethoscope and a smartphone

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

Auscultation

Auscultation at five auscultation points using a digital stethoscope and a smartphone

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

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
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

Meet the organizations funding or collaborating on the study and learn about their roles.

University of Erlangen-Nürnberg Medical School

OTHER

Sponsor Role collaborator

University Hospital Erlangen

OTHER

Sponsor Role collaborator

Friedrich-Alexander-Universität Erlangen-Nürnberg

OTHER

Sponsor Role lead

Responsible Party

Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.

Johannes Michael Altstidl

Physician

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

Explore where the study is taking place and check the recruitment status at each participating site.

Deparment of Medicine 2 - Cardiology and Angiology, Friedrich-Alexander-Universität Erlangen-Nürnberg

Erlangen, , Germany

Site Status

Countries

Review the countries where the study has at least one active or historical site.

Germany

Other Identifiers

Review additional registry numbers or institutional identifiers associated with this trial.

23-39-B

Identifier Type: -

Identifier Source: org_study_id