Artificial Intelligence (AI) Analysis of Synchronized Phonocardiography (PCG) and Electrocardiogram(ECG)

Shanghai Jiao Tong University

Status

Enrolling

Conditions

Heart Failure

Study type

Observational

Funder types

Other

Identifiers

NCT06009718

RJH-PEG

Details and patient eligibility

About

The diagnosis of depressed left ventricular ejection fraction (dLVEF) (EF<50%) 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.

Full 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 failure(HF).

This study is a Case-control study. In this study, the investigators seek to develop an artificial intelligence (AI) analysis system to identify dLVEF (EF<50%) 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.

Enrollment

3,000 estimated patients

Sex

All

Ages

18 to 100 years old

Volunteers

No Healthy Volunteers

Inclusion criteria

Attendance at RuiJin hospital for UCG
Signed dated informed consent
Commit to follow the research procedures and cooperate in the implementation of the whole process research
UCG has been completed
Age ≥ 18
At least 8 consecutive cycles of sinus rhythm can be recorded

Exclusion criteria