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

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.

Study Overview

• Status: Recruiting
• Conditions: Aortic Valve Stenosis
• Intervention / Treatment: Diagnostic test: Auscultation

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.

• Study Type: Observational
• Enrollment (Estimated): 100

Contacts and Locations

Study Locations

• Germany
  • Erlangen, Germany, 91054
    • Recruiting
    • Deparment of Medicine 2 - Cardiology and Angiology, Friedrich-Alexander-Universität Erlangen-Nürnberg
    • Contact: Stephan Achenbach, Prof. Dr. med.; Phone Number: +49 9131 85 35000; Email: med2-direktion@uk-erlangen.de
    • Sub-Investigator: Johannes Michael Altstidl, Dr. med.
    • Sub-Investigator: Lars Anneken, Dr. med.
    • Principal Investigator: Stephan Achenbach, Prof. Dr. med.
    • Sub-Investigator: Thomas Robert Altstidl
    • Principal Investigator: Björn M. Eskofier, Prof. Dr.

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Probability Sample

Study Population

No relevant heart valve disease or severe aortic stenosis with no other relevant heart valve disease

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Severe Aortic Stenosis	Diagnostic test: Auscultation; Auscultation at five auscultation points using a digital stethoscope and a smartphone
No Relevant Heart Valve Disease	Diagnostic test: Auscultation; Auscultation at five auscultation points using a digital stethoscope and a smartphone

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Algorithm Performance	Performance of algorithmic diagnosis measured by accuracy, sensitivity, specificity, and positive predictive value	Baseline

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Comparison with Digital Stethoscope	Comparison of algorithm performance using smartphone recordings with algorithm performance using digital stethoscope recordings	Baseline
Comparison of Auscultation Points	Comparison of algorithm performance using different sets of auscultation points	Baseline

Collaborators and Investigators

• Sponsor: Friedrich-Alexander-Universität Erlangen-Nürnberg
• Collaborators: University of Erlangen-Nürnberg Medical School

Study record dates

Study Major Dates

• Study Start (Actual): March 9, 2023
• Primary Completion (Estimated): July 1, 2024
• Study Completion (Estimated): October 1, 2024

Study Registration Dates

• First Submitted: May 5, 2024
• First Submitted That Met QC Criteria: May 5, 2024
• First Posted (Actual): May 8, 2024

Study Record Updates

• Last Update Posted (Actual): May 8, 2024
• Last Update Submitted That Met QC Criteria: May 5, 2024
• Last Verified: May 1, 2024

More Information

Terms related to this study

• Keywords: Digital Health; Cardiology; Machine Learning; Heart Valve Disease
• Additional Relevant MeSH Terms: Heart Diseases; Cardiovascular Diseases; Pathological Conditions, Anatomical; Aortic Valve Disease; Heart Valve Diseases; Ventricular Outflow Obstruction; Aortic Valve Stenosis; Constriction, Pathologic
• Other Study ID Numbers: 23-39-B

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: Recordings and metadata are published
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; ICF; ANALYTIC_CODE

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No