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

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

Conditions

• Kawasaki Disease
• Chest X-ray for Clinical Evaluation
• Mucocutaneous Lymph Node Syndrome

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.

Sponsors & Collaborators

• Children's Hospital of Soochow University

  collaborator OTHER

• Hunan Provincial People's Hospital

  collaborator OTHER

• Women and Children Hospital of Qinghai Province

  collaborator OTHER

• Yangzhou No.1 People's Hospital

  collaborator OTHER

• Xinhua Hospital, Shanghai Jiao Tong University School of Medicine

  lead OTHER

Principal Investigators

• Kun Sun, Doctoral degree · Xinhua hospital affiliated with Shanghai Jiao Tong university school of medicine

Eligibility

Min Age

0 Years

Max Age

18 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2026-02-01

Primary Completion

2026-12-31

Completion

2027-12-31

Countries

• China

Study Locations