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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
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
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