Ignite Creation Date:
2024-05-06 @ 7:26 PM
Last Modification Date:
2024-10-26 @ 3:06 PM
Study NCT ID:
NCT06009718
Status:
RECRUITING
Last Update Posted:
2023-08-24
First Post:
2023-07-06
Brief Title:
Artificial Intelligence AI Analysis of Synchronized Phonocardiography PCG and ElectrocardiogramECG
Sponsor:
Ruijin Hospital
Organization:
Ruijin Hospital
Study Overview
Official Title:
A Deep-learning-based Multi-modal PhonocardiogramPCG and ElectrocardiogramECG Processing Framework for Screening Depressed Left Ventricular Ejection Fraction dLVEF Using a Wearable Cardiac Patch
Status:
RECRUITING
Status Verified Date:
2023-08
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:
The diagnosis of depressed left ventricular ejection fraction dLVEF EF50 depends on golden standard ultrasound cardiography UCG A wearable synchronized phonocardiography PCG and electrocardiogram ECG device can assist in the diagnosis of dLVEF which can both expedite access to life-saving therapies and reduce the need for costly testing
Detailed Description:
The synchronized PCG and ECG is wirelessly paired with the WenXin Mobile application allowing for simultaneous recording and visualization of PCG and ECG These features uniquely enable this device to accumulate large sets of acoustic data on patients both with and without heart failureHF
This study is a Case-control study In this study the investigators seek to develop an artificial intelligence AI analysis system to identify dLVEF EF50 by PCG and ECG All adults aged 18 years planned for UCG were eligible to participate inpatients and outpatients Specifically the investigators will attempt to develop machine learning algorithms to learn synchronized PCG and ECG of patients with dLVEF Then we use these algorithms to identify dLVEF subjects The investigators anticipate to demonstrate the wearable cardiac patch with synchronized PCG and ECG can reliably and accurately diagnose dLVEF in the primary care setting
This study is a Case-control study In this study the investigators seek to develop an artificial intelligence AI analysis system to identify dLVEF EF50 by PCG and ECG All adults aged 18 years planned for UCG were eligible to participate inpatients and outpatients Specifically the investigators will attempt to develop machine learning algorithms to learn synchronized PCG and ECG of patients with dLVEF Then we use these algorithms to identify dLVEF subjects The investigators anticipate to demonstrate the wearable cardiac patch with synchronized PCG and ECG can reliably and accurately diagnose dLVEF in the primary care setting
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