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

Last updated: January 14, 2025
Sponsor: Ruijin Hospital
Overall Status: Active - Recruiting

Phase

N/A

Condition

Congestive Heart Failure

Chest Pain

Heart Failure

Treatment

N/A

Clinical Study ID

NCT06009718
RJH-PEG
  • Ages 18-100
  • All Genders

Study 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.

Eligibility Criteria

Inclusion

Inclusion Criteria:

  • Attendance at RuiJin hospital for UCG

  • Signed dated informed consent

  • Commit to follow the research procedures and cooperate in the implementation of thewhole process research

  • UCG has been completed

  • Age ≥ 18

  • At least 8 consecutive cycles of sinus rhythm can be recorded

Exclusion

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

Study Design

Total Participants: 3000
Study Start date:
August 25, 2023
Estimated Completion Date:
June 01, 2028

Study 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.

Connect with a study center

  • Ruijin Hospital, Shanghai Jiaotong School of Medicine

    Shanghai, 200025
    China

    Active - Recruiting

  • Shanghai Chest Hospital, Shanghai Jiao Tong University School Of Medicine

    Shanghai, 200030
    China

    Active - Recruiting

  • Shanghai East Hospital

    Shanghai, 200123
    China

    Active - Recruiting

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