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Artificial intelligence may detect Children's heart disease from a routine ECG

NCT ID NCT06383546

Diagnosis Sponsor: Xinhua Hospital, Shanghai Jiao Tong University School of Medicine Source: ClinicalTrials.gov ↗

What the study statuses mean

This study's is highlighted.

Recruitment status, easiest to join first

Recruiting now
This trial is taking on new participants right now.
Not yet recruiting
Registered, but not yet taking participants.
By invitation only
Not open to general applications. Only people the study team invites can take part.
Paused
Paused for now. It may or may not start again.
Ongoing
Running, but no longer taking on new participants.
Completed
The trial has finished. Results may not be published yet.
Stopped early
Stopped early, before it reached the end. That can be for many reasons, including safety.
Cancelled This study
Cancelled before anyone took part.

Expanded access (not trials)

Expanded access
Not a trial. This treatment can be requested outside a study, case by case, for people who qualify.
Expanded access (paused)
Not a trial. The treatment can normally be requested outside a study, but is unavailable right now.
Expanded access (ended)
Not a trial. The treatment could once be requested outside a study, but no longer can.
Approved
The treatment has been approved, so it is available normally rather than through this programme.

When the status isn't known

Details not published
The full record has not been published yet, so there is little to show here.
Status unknown
This status has not been confirmed recently, so it may be out of date.

First seen Jul 29, 2026 · Last updated Jul 30, 2026 · Updated 1 time

Summary

This trial is developing an artificial intelligence system that reads electrocardiograms (ECGs) to detect congenital heart disease in children. Researchers are collecting ECG data from thousands of children, including those with common and rare heart defects, to train a deep-learning model. The goal is to create a fast, affordable screening tool that could help catch heart problems earlier, especially in settings where advanced imaging is not readily available.

What this could mean

Our plain-language read of the trial. This is informational only, not medical advice or a prediction.

Active substance
an artificial intelligence system that analyzes electrocardiograms (ECGs) to detect congenital heart disease in children
What this could lead to
If it works, this could provide a fast, low-cost screening tool to catch congenital heart disease earlier in children, potentially improving outcomes and reducing mortality.
What could go wrong
This is an early-stage study focused on building and testing the AI model, not a large clinical trial. The system may not perform well across all types of heart defects or in real-world settings.

This is an AI summary of the original study and may miss details. Read our disclaimer.

Study facts

What this study's own registry entry says, in plain language.

Started

Jan 2024

Expected to finish

Dec 2028

An estimate. End dates often move.

Lead sponsor

Other sponsor

The registry's catch-all category, for sponsors it does not file as a company, a government agency, or a research network.

Who can take part

This study's own entry requirements. Only the study team can say for certain whether you qualify.

Who is studied

Our dataset consisted of retrospective data from patients aged under 18 years who had complete ECG, echocardiography, examination and medical history information.

Ages

3 months to 18 years

Sex

Anyone

Healthy volunteers

Accepted

You do not need to have the condition being studied to take part.

Show the full entry requirements

Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.

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;

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Conditions

The condition(s) this trial relates to.

atrial septal defect congenital heart disease disease pulmonary arterial hypertension

As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

Artificial intelligence Congenital heart disease in children Deep learning Diagnosis Electrocardiogram

Contacts and locations

Locations

  • Xinhua Hospital, Shanghai Jiao Tong University School of Medicine

    Shanghai, Shanghai Municipality, China

More trials for these conditions

Other studies related to the condition(s) this trial covers.