Ignite Creation Date:
2024-05-11 @ 8:31 AM
Last Modification Date:
2024-10-26 @ 3:29 PM
Study NCT ID:
NCT06404437
Status:
RECRUITING
Last Update Posted:
2024-05-08
First Post:
2024-05-05
Brief Title:
Detection of Aortic Stenosis With Smartphone Auscultation Using Machine Learning HEARTBEAT-Pilot
Sponsor:
Friedrich-Alexander-Universität Erlangen-Nürnberg
Organization:
Friedrich-Alexander-Universität Erlangen-Nürnberg
Study Overview
Official Title:
Detection of Aortic Stenosis With Smartphone Auscultation Using Machine Learning HEARTBEAT-Pilot
Status:
RECRUITING
Status Verified Date:
2024-05
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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
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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None