Using AI to detect congenital heart disease in children through ECG

Artificial Intelligence-enabled ECG Detection of Congenital Heart Disease in Children: a Novel Diagnostic Tool

This study is testing if using artificial intelligence with heart scans can help doctors find congenital heart disease in children faster and more accurately.

Quick facts

• Study type: Observational
• Enrollment: 30000 (estimated)
• Ages: 3 Months to 18 Years
• Sex: All
• Sponsor: Xinhua Hospital, Shanghai Jiao Tong University School of Medicine (Academic / other)
• Locations: 1 site (Shanghai, Shanghai)
• Trial ID: NCT06383546 on ClinicalTrials.gov

What this trial studies

This study focuses on the early detection and diagnosis of congenital heart disease (CHD) in children using artificial intelligence (AI) and electrocardiogram (ECG) technology. By leveraging deep learning algorithms, the research aims to improve the accuracy of ECG readings, which are crucial for identifying CHD. The study will involve children aged 3 months to 18 years, comparing those with confirmed CHD against a control group with normal heart structures. The goal is to enhance diagnostic capabilities and ultimately improve patient outcomes.

Who should consider this trial

Good fit: Ideal candidates for this study are children aged 3 months to 18 years who have been diagnosed with specific types of congenital heart disease or have normal heart structures.

Not a fit: Patients with major illnesses or complex heart malformations may not benefit from this study.

Why it matters

Potential benefit: If successful, this approach could lead to earlier and more accurate diagnoses of congenital heart disease in children, potentially saving lives.

How similar studies have performed: Other studies have shown promise in using AI for medical diagnostics, suggesting that this approach could be effective, although this specific application is relatively novel.

Eligibility criteria

Inclusion Criteria:

* The age of first visit was from 3 months after birth to 18 years old;
* In the atrial septal defect group, patients in the case group were required to complete ECG examination and confirmed by careful cardiac ultrasonography that there was a simple secondary atrial septal defect without other complex heart malformations (such as ectopic pulmonary vein drainage, trunk conus artery malformation, interrupted aortic arch, primary pulmonary hypertension, etc.). In the pulmonary hypertension group, the presence of CHD associated pulmonary hypertension was confirmed by careful cardiac ultrasonography examination. The control group was the patients with normal intracardiac structure examined by cardiac ultrasonography. The time interval between ECG examination and echocardiography examination of all patients was \< 1 month;
* No major illness at the time of initial visit (non-life-threatening organic disease caused by congenital heart disease).

Exclusion Criteria:

* Age of first visit \< 3 months or \> 18 years old;
* Complicated congenital heart disease (such as anomalous pulmonary venous drainage, trunk conus artery malformation, interrupted aortic arch, primary pulmonary hypertension, etc.);
* The clinical information is incomplete, including the lack of ECG or echocardiography information, or the time interval between ECG and echocardiography is \> 1 month;
* Life-threatening diseases associated with other organ systems;

Where this trial is running

Shanghai, Shanghai

• Xinhua Hospital, Shanghai Jiao Tong University School of Medicine — Shanghai, Shanghai, China (Recruiting)

Study contacts

• Study coordinator: Sun Jing, MD

Conditions Artificial Intelligence; Electrocardiogram; Deep Learning; Congenital Heart Disease in Children; Diagnosis