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
Case group
The age of seeking medical treatment is less than or equal to 18 years old; ·The medical record system diagnosis contains the diagnosis of "Kawasaki Disease", "mucocutaneous lymph node syndrome" or "IVIG non-response Kawasaki disease"
At least one complete chest X-ray examination data (images and reports) is available during the same hospitalization
Control group
Case group
Chest X-ray quality issues: Severe artifacts, overexposure/underexposure leading to inability to assess key structures
Incomplete clinical information, including lack of chest X-ray examination, laboratory tests, and unclear days of fever Inability to determine the final diagnosis (such as loss to follow-up, diagnosis in doubt)
Control group
The case group consists of children diagnosed with Kawasaki Disease (KD) over the past 10 years. The inclusion criteria include: Children diagnosed with Kawasaki Disease based on clinical symptoms and confirmed by medical records. The control group consists of data from patients with fever lasting ≥3 days, matched to the KD cohort based on diagnosis year, month, and clinical characteristics. The study focuses on examining the relationship between chest X-ray features and Kawasaki Disease in these patients.
Other
Retrospective
AI-Based Early Warning System for Kawasaki Disease
Diagnostic testThis 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.
Area Under Curve
sensitivity
specificity
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Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
Lead sponsor
Children's Hospital of Soochow University
Collaborator
Hunan Provincial People's Hospital
Collaborator
Women and Children Hospital of Qinghai Province
Collaborator
Yangzhou No.1 People's Hospital
Collaborator
This trial is not recruiting at the moment. You can still explore other options:
