Using AI to analyze heart sounds and electrical signals for heart failure diagnosis
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
This study is testing if a new AI system can help doctors diagnose heart failure by analyzing heart sounds and electrical signals from patients who are already getting heart ultrasound tests.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 3000 (estimated) |
| Ages | 18 Years to 100 Years |
| Sex | All |
| Sponsor | Ruijin Hospital Academic / other |
| Locations | 3 sites (Shanghai and 2 other locations) |
| Trial ID | NCT06009718 on ClinicalTrials.gov |
What this trial studies
This observational study aims to develop an artificial intelligence (AI) system that analyzes synchronized phonocardiography (PCG) and electrocardiogram (ECG) data to identify patients with depressed left ventricular ejection fraction (dLVEF). Participants, aged 18 and older, who are scheduled for ultrasound cardiography (UCG) at RuiJin Hospital, will wear a device that records PCG and ECG simultaneously. The study seeks to create machine learning algorithms that can accurately diagnose dLVEF, potentially improving access to timely treatments and reducing the need for expensive testing. By accumulating large datasets of acoustic data, the study aims to enhance diagnostic capabilities in primary care settings.
Who should consider this trial
Good fit: Ideal candidates are adults aged 18 and older who are attending RuiJin Hospital for ultrasound cardiography.
Not a fit: Patients with pacemakers or significant conduction abnormalities may not benefit from this study.
Why it matters
Potential benefit: If successful, this approach could lead to faster and more accurate diagnoses of heart failure, enabling timely interventions.
How similar studies have performed: While the use of AI in cardiac diagnostics is emerging, this specific approach of combining PCG and ECG analysis is relatively novel.
Eligibility criteria
Show full inclusion / exclusion criteria
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
Where this trial is running
Shanghai and 2 other locations
- Ruijin Hospital, Shanghai Jiaotong School of Medicine — Shanghai, China (Recruiting)
- Shanghai Chest Hospital, Shanghai Jiao Tong University School Of Medicine — Shanghai, China (Recruiting)
- Shanghai East Hospital — Shanghai, China (Recruiting)
Study contacts
- Principal investigator: Ruiyan Zhang, MD, PhD — Ruijin Hospital, Shanghai Jiaotong School of Medicine
- Study coordinator: Wenli Zhang, MD
- Email: zwl11929@rjh.com.cn
- Phone: +86 21 13917615339
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.