Clinical trial NCT06383546: Artificial Intelligence-enabled ECG Detection of Congenital Heart Disease in Children: a Novel Diagnostic Tool
Official title: Artificial Intelligence-enabled ECG Detection of Congenital Heart Disease in Children: a Novel Diagnostic Tool
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Artificial Intelligence
Electrocardiogram
Deep Learning
Congenital Heart Disease in Children
Diagnosis
Lead sponsor: Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
Sponsor class: OTHER
First posted: 2024-04-25
Enrollment: 30000
Sex: ALL
Minimum age (years): 3
Maximum age (years): 18
Standard ages: CHILD
ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Our dataset consisted of retrospective data from patients aged under 18 years who had complete ECG, echocardiography, examination and medical history information.

Brief 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. 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.


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;

Primary outcomes:
measure: Large-scale ECG database for children
timeFrame: 2024.01.01-2024.12.30
measure: Artificial intelligence-assisted electrocardiogram model for CHD in Children
timeFrame: 2024.01.01-2025.12.30
Secondary outcomes:
Other outcomes:
Locations:
city: Shanghai
country: China
facility: Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
state: Shanghai
Study officials:
References: