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

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

NCT Number	NCT07405658
Lead Sponsor	Xinhua Hospital, Shanghai Jiao Tong University School of Medicine Collaborators: Children's Hospital of Soochow University, Hunan Provincial People's Hospital, Women and Children Hospital of Qinghai Province, Yangzhou No.1 People's Hospital
Conditions	Kawasaki Disease, Chest X-ray for Clinical Evaluation, Mucocutaneous Lymph Node Syndrome
Enrollment	20,000 participants
Start Date	2026-02-01
Primary Completion	2026-12-31 (estimated)
Study Completion	2027-12-31 (estimated)
Updated on ClinicalTrials.gov	2026-02-12