Clinical Study on an Artificial Intelligence-Assisted Chest Radiograph Model Based on Big Data and Deep Learning for Early Detection of Kawasaki Disease
Starting soon
Conditions studied: Kawasaki Disease, Chest X-ray for Clinical Evaluation, Mucocutaneous Lymph Node Syndrome
In brief
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 facts
- Study ID
- NCT07405658
- Run by
- Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
- People needed
- 20000
- Starts
- 2026-02-01
- Expected to finish
- 2027-12-31
- Last updated by the study team
- 2026-02-12
Who can join
Age: any, up to 18. Sex: any. Healthy volunteers: accepted.
You may qualify if…
- 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
- 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
You may not qualify if…
- 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
- 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)
Where it is running
- Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine — Shanghai, Shanghai Municipality, China
Full record on ClinicalTrials.gov
Trial information comes from ClinicalTrials.gov and is refreshed daily. TrialsForMe does not provide medical care and does not run the studies it lists.