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

Sponsor

Ruijin Hospital (Other)

Overall Status

Recruiting

CT.gov ID

NCT06009718

Collaborator

(none)

3,000

Enrollment

Location

Anticipated Duration (Months)

31.3

Patients Per Site Per Month

Study Details

Study Description

Brief Summary

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.

Condition or Disease	Intervention/Treatment	Phase
Heart Failure

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 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.

Study Design

Study Type:

Observational

Anticipated Enrollment :

3000 participants

Observational Model:

Case-Control

Time Perspective:

Prospective

Official Title:

A Deep-learning-based Multi-modal Phonocardiogram(PCG) and Electrocardiogram(ECG) Processing Framework for Screening Depressed Left Ventricular Ejection Fraction (dLVEF) Using a Wearable Cardiac Patch

Actual Study Start Date :

Jun 1, 2020

Anticipated Primary Completion Date :

Jun 1, 2027

Anticipated Study Completion Date :

Jun 1, 2028

Arms and Interventions

Arm	Intervention/Treatment
Model training group Compare the results of PCG and ECG with UCG, and conduct model training analysis
Model validation group Compare the results of PCG and ECG with UCG, and conduct model validation analysis

Outcome Measures

Primary Outcome Measures

Determination of Heart Failure Disease [one time assessment at baseline (approx. 5 minutes)]
Heart Failure Disease was determined by EMAT （millisecond, ms）calculate from synchronized PCG and ECG signals using an artificial intelligence (AI) guided model.

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years to 100 Years

Sexes Eligible for Study:

All

Accepts 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:

Patients with pacemakers
Complete left bundle branch block or block or QRS wave widening>120ms
Left chest skin damaged or allergic to patch
Refusal to participate

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	Ruijin Hospital, Shanghai Jiaotong School of Medicine	Shanghai		China

Sponsors and Collaborators

Ruijin Hospital

Investigators

Principal Investigator: Ruiyan Zhang, MD, PhD, Ruijin Hospital, Shanghai Jiaotong School of Medicine
Study Director: Wenli Zhang, MD, Ruijin Hospital, Shanghai Jiaotong School of Medicine
Study Chair: Bei Song, MD, Ruijin Hospital, Shanghai Jiaotong School of Medicine