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Starting soon Observational

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

NCT07405658 · tracked via the Priya Life Science China tracker
Phase
Observational
Started
2026-02-01
Last updated
2026-02-12

Condition(s) studied

Kawasaki DiseaseChest X-ray for Clinical EvaluationMucocutaneous Lymph Node Syndrome

Investigational drug(s) / intervention(s)

AI-Based Early Warning System for Kawasaki Disease

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.

Study 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

Eligibility

Sex
ALL
Min age
0 Years
Max age
18 Years
Healthy volunteers
Accepted
Inclusion Criteria: 1. 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 2. Control group * The age of seeking medical treatment is less than or equal to 18 years old * The same period as the case group * Fever lasts for 3 days or more * Rule out the possibility of diagnosing Kawasaki disease Exclusion Criteria: 1. 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) 2. Control 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 make a clear final diagnosis (such as loss to follow-up, questionable diagnosis)

Primary outcome measure(s)

  • Area Under Curve — Up to 14 days after fever onset
  • sensitivity — Up to 14 days after fever onset
  • specificity — Up to 14 days after fever onset

Trial sites (1)

FacilityCityRegionStatus
Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine Shanghai Shanghai Municipality
Official registry record

This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.

View NCT07405658 on ClinicalTrials.gov ↗ ← All trials in China