Artificial IntelligenceAI-ECG-CHDNCT06383546
Artificial Intelligence-enabled ECG Detection of Congenital Heart Disease in Children: a Novel Diagnostic Tool
An observational study of Artificial Intelligence and Electrocardiogram, run by Xinhua Hospital, Shanghai Jiao Tong University School of Medicine at 1 site in China.
Trial summary
Congenital heart disease (CHD) is the most common congenital disease in children. The early detection, diagnosis and treatment of CHD in children is of great significance to improve the prognosis and reduce the mortality of children, but the current screening methods have limitations. Read more about this study
- Who is it for?
- Ages 3 months to 18 yearsHealthy volunteers can take part
- Conditions the study is for
- Artificial Intelligence, Electrocardiogram, Deep Learning
- How many people
- 0 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2024to 30 Dec 2028
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 3 months to 18 years
- All sexes
- Healthy volunteers accepted
- Child, Adult
Study population: Our dataset consisted of retrospective data from patients aged under 18 years who had complete ECG, echocardiography, examination and medical history information.
Who may be able to join
- 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).
Who cannot join
- 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;
Trial locations and contacts
1 site in 1 country.
China1 site
Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
Shanghai, Shanghai Municipality
About this study
Congenital heart disease (CHD) is the most common congenital disease in children. The early detection, diagnosis and treatment of CHD in children is of great significance to improve the prognosis and reduce the mortality of children, but the current screening methods have limitations. Electrocardiogram (ECG), as an economical and rapid means of heart disease detection, has a very important value in the auxiliary diagnosis of CHD.Big data and deep learning technologies in artificial intelligence (AI) have shown great potential in the medical field. The advent of the big data era provides rich data resources for the in-depth study of CHD ECG signals in children. The development of deep learning technology, especially the breakthrough in the field of image recognition, provides a strong technical support for the intelligent analysis of electrocardiogram. The particularity of children electrocardiogram requires the development of a special algorithm model. At present, the research on the application of deep learning models to identify children's electrocardiograms is limited, and the training and verification from large data sets are lacking. Based on the Chinese Congenital Heart Disease Collaborative Research Network, this project aims to integrate data and deep learning technology to develop a set of intelligent electrocardiogram assisted diagnosis system (CHD-ECG AI system) suitable for children with CHD, so as to improve the early detection rate of CHD and improve the efficiency of congenital heart disease screening.
- Conditions
- Artificial Intelligence, Electrocardiogram, Deep Learning, Congenital Heart Disease in Children, Diagnosis
- Medical subject headings
- Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Atrial septal defect | Not described | |
| Pulmonary hypertension | Not described | |
| Control | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
Large-scale ECG database for children
The ECG data of children from multiple centers were collected and collated, including common and rare CHD types and normal children's ECG, to construct a large-scale ECG database covering different ages and CHD diseases. In addition, the original ECG data (digital signals or ECG images) will be pre-processed to make it conform to the input standards of deep learning models, so as to improve the quality and efficiency of subsequent model training and reduce the heterogeneity of multi-center ECG data.
Measured: 2024.01.01-2024.12.30
Artificial intelligence-assisted electrocardiogram model for CHD in Children
The deep neural network model will be established based on algorithms such as convolutional neural network, transformers and Autoencoders, and will be trained and verified in the multi-center children's ECG dataset (85%) established based on CCHDnet, so as to continuously optimize the model and improve the diagnostic performance of the model. Further, the deep learning model based on the single disease of CHD will be integrated, and the CHD-ECG AI system will be built, and the model will eventually automatically extract and recognize the general basic information such as the age and gender of the child through the ECG, and then predict and classify the potential CHD characteristics in the ECG based on this. The research group initially selected the representative subtypes of CHD - atrial septal defect and pulmonary hypertension as the initial direction of exploration.
Measured: 2024.01.01-2025.12.30
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Apr 2024
- Study start1 Jan 2024
- Primary completion30 Dec 2028 (estimated)
- Study completion30 Dec 2028 (estimated)
Why it stopped: Repeated application of the same project
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Artificial IntelligenceStudies of the same condition, open and under way.
- More trials run by Xinhua Hospital, Shanghai Jiao Tong University School of MedicineThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 0 participants (actual)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Jing Sun, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06383546
- Sponsor's protocol ID
- XHEC-C-2024-053-1
- Acronym
- AI-ECG-CHD
- Status last verified
- Apr 2026
- First submitted
- 22 Apr 2024
- Last updated
- 29 Jul 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 29 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-enabled ECG Detection of Congenital Heart Disease in Children: a Novel Diagnostic Tool
- ClinicalTrials.gov ID
- NCT06383546
- Acronym / sponsor ID
- AI-ECG-CHD / XHEC-C-2024-053-1
- Conditions
- Artificial Intelligence, Electrocardiogram, Deep Learning, Congenital Heart Disease in Children, Diagnosis
- Study type
- Observational
- Recruitment status
- WithdrawnReason stopped: Repeated application of the same project
- Enrolment
- 0 participants (actual)
- Lead sponsor
- Xinhua Hospital, Shanghai Jiao Tong University School of MedicineUniversity, hospital or other
- Study start
- 1 Jan 2024 (actual)
- Primary completion
- 30 Dec 2028 (estimated)
- Study completion
- 30 Dec 2028 (estimated)
- First posted
- 25 Apr 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Congenital heart disease (CHD) is the most common congenital disease in children. The early detection, diagnosis and treatment of CHD in children is of great significance to improve the prognosis and reduce the mortality of children, but the current screening methods have limitations. Electrocardiogram (ECG), as an economical and rapid means of heart disease detection, has a very important value in the auxiliary diagnosis of CHD.Big data and deep learning technologies in artificial intelligence (AI) have shown great potential in the medical field. The advent of the big data era provides rich data resources for the in-depth study of CHD ECG signals in children. The development of deep learning technology, especially the breakthrough in the field of image recognition, provides a strong technical support for the intelligent analysis of electrocardiogram. The particularity of children electrocardiogram requires the development of a special algorithm model. At present, the research on the application of deep learning models to identify children's electrocardiograms is limited, and the training and verification from large data sets are lacking. Based on the Chinese Congenital Heart Disease Collaborative Research Network, this project aims to integrate data and deep learning technology to develop a set of intelligent electrocardiogram assisted diagnosis system (CHD-ECG AI system) suitable for children with CHD, so as to improve the early detection rate of CHD and improve the efficiency of congenital heart disease screening.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Atrial septal defect | – | – |
| Pulmonary hypertension | – | – |
| Control | – | – |
Who may qualify
- Ages
- 3 months to 18 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult
- Study population
- Our dataset consisted of retrospective data from patients aged under 18 years who had complete ECG, echocardiography, examination and medical history information.
Key inclusion criteria (3 listed)
- 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).
Key exclusion criteria (4 listed)
- 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;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Large-scale ECG database for children
Time frame: 2024.01.01-2024.12.30
The ECG data of children from multiple centers were collected and collated, including common and rare CHD types and normal children's ECG, to construct a large-scale ECG database covering different ages and CHD diseases. In addition, the original ECG data (digital signals or ECG images) will be pre-processed to make it conform to the input standards of deep learning models, so as to improve the quality and efficiency of subsequent model training and reduce the heterogeneity of multi-center ECG data.
Artificial intelligence-assisted electrocardiogram model for CHD in Children
Time frame: 2024.01.01-2025.12.30
The deep neural network model will be established based on algorithms such as convolutional neural network, transformers and Autoencoders, and will be trained and verified in the multi-center children's ECG dataset (85%) established based on CCHDnet, so as to continuously optimize the model and improve the diagnostic performance of the model. Further, the deep learning model based on the single disease of CHD will be integrated, and the CHD-ECG AI system will be built, and the model will eventually automatically extract and recognize the general basic information such as the age and gender of the child through the ECG, and then predict and classify the potential CHD characteristics in the ECG based on this. The research group initially selected the representative subtypes of CHD - atrial septal defect and pulmonary hypertension as the initial direction of exploration.
Results and safety
The study was withdrawn before enrolling participants, so there are no results.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Jul 2026.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Artificial Intelligence and Electrocardiogram, run by Xinhua Hospital, Shanghai Jiao Tong University School of Medicine at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: withdrawn, as of the sponsor's last update on 29 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study was withdrawn before enrolling participants, so there are no results.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Large-scale ECG database for children (time frame: 2024.01.01-2024.12.30). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 29 Jul 2026.