NCT06383546

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

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 30,000 (estimated)
• Sponsor: Xinhua Hospital, Shanghai Jiao Tong University School of Medicine · Academic / Other
• Sex: All
• Age: 3 Months – 18 Years
• Healthy volunteers: Accepted

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.

Conditions

• Artificial Intelligence
• Electrocardiogram
• Deep Learning
• Congenital Heart Disease in Children
• Diagnosis

Timeline

• Start date: 2024-01-01
• Primary completion: 2024-12-30
• Completion: 2025-12-30
• First posted: 2024-04-25
• Last updated: 2024-04-25

Locations

1 site across 1 country: China