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NCT07405658

Clinical Study on an Artificial Intelligence-Assisted Chest Radiograph Model Based on Big Data and Deep Learning for Early Detection of Kawasaki Disease

Sponsor: Xinhua Hospital, Shanghai Jiao Tong University School of Medicine

View on ClinicalTrials.gov

Summary

The goal of this observational study is to develop an AI-based early warning system for Kawasaki Disease (KD) using chest X-rays (CXR) in children diagnosed with Kawasaki Disease. The main question\[s\] it aims to answer are: 1. Can AI modeling of CXR features help identify high-risk KD patients earlier than current diagnostic methods? 2. Can the AI system predict the optimal IVIG treatment window and coronary artery risks in KD patients? Participants will: Provide retrospective data on chest X-rays and clinical data (CRP, coronary ultrasound, etc.) Allow analysis of CXR features using deep learning models to extract relevant patterns Have their data incorporated into a federated learning model to ensure privacy and data security

Key Details

Gender

All

Age Range

0 Years - 18 Years

Study Type

OBSERVATIONAL

Enrollment

20000

Start Date

2026-02-01

Completion Date

2027-12-31

Last Updated

2026-02-12

Healthy Volunteers

Yes

Interventions

DIAGNOSTIC_TEST

AI-Based Early Warning System for Kawasaki Disease

This study utilizes an AI-based early warning system for Kawasaki Disease (KD) to predict the optimal IVIG treatment window and assess coronary risk. The system analyzes chest X-ray (CXR) images and integrates them with clinical data such as CRP levels and clinical symptoms. The intervention involves the development of a multi-modal dynamic prediction model that uses a dual-pathway convolutional neural network (CNN) to extract relevant CXR features and a graph neural network to integrate laboratory indicators. The AI system outputs a prediction of the IVIG treatment window and estimates the risk of coronary artery damage. This early warning system aims to reduce diagnosis time and improve treatment outcomes by identifying high-risk KD patients earlier, enabling timely intervention and personalized treatment plans. The model is designed to be lightweight (under 50MB) to be easily applicable in primary care settings.

Locations (1)

Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine

Shanghai, Shanghai Municipality, China