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

Study Overview

• Status: Not yet recruiting
• Conditions: Kawasaki Disease; Mucocutaneous Lymph Node Syndrome; Chest X-ray for Clinical Evaluation
• Intervention / Treatment: Diagnostic test: AI-Based Early Warning System for Kawasaki Disease

Detailed Description

1. Research Background and Clinical Pain Points Kawasaki Disease (KD) is a leading cause of acquired heart disease in children. Traditional diagnosis relies on subjective symptoms such as fever lasting ≥5 days and rashes, leading to two major problems: delayed diagnosis, with 30% of atypical patients missing the optimal IVIG treatment window (fever duration of 5-10 days); and coronary artery damage: delaying treatment for ≥7 days increases the risk of coronary dilation by 47%. The current AHA standards have only a 35% sensitivity for children with fever ≤3 days, highlighting the urgent need to establish an objective early warning system.

2. Research Objectives and Technical Approach Core breakthrough: First time using routine chest X-rays (CXR) to develop an AI-based early warning model.

   Technical path: Multi-center data integration, collection of CXR and clinical data (clinical symptoms, laboratory tests, coronary ultrasound, etc.), and a federated learning framework to ensure privacy and security. Exploration of imaging biomarkers and CXR features that are invisible to the human eye, as well as the development of a multi-modal dynamic early warning model.

   Dual-path CNN to extract CXR features → Graph neural networks to integrate laboratory indicators → Diagnosis model to output the risk score of kawasaki disease.↑

3. Innovation Advantages and Clinical Value Early-warning performance was strong by day 3 of fever, achieving a pre-trial sensitivity of 87.2% for Kawasaki disease, while providing individualized IVIG treatment windows and predicted coronary-artery risk. The lightweight model (less than 50MB) is adaptable for use in primary care settings.

   Clinical pathway:

   AI identifies high-risk children → Priority for echocardiography → IVIG treatment window advanced → Reduction in cardiovascular complications.

   The ultimate goal is to shorten diagnosis time and reduce cardiovascular complications of KD patients in China.

4. Validation Plan and Results Translation

   Three-phase validation:

   Internal: 5-fold cross-validation (AUC ≥0.88) External: Blind testing in 3 hospitals (sensitivity >85%, specificity >80%) Clinical: Real-time deployment in emergency settings (response time ≤15 seconds) Results translation: 1-2 peer-reviewed journal publications and 1-2 patent filings; facilitating early identification of Kawasaki disease, thereby improving clinical outcomes.

5. Key Conclusion This study aims to decode objective biomarkers such as pulmonary artery vascular signs in CXR images and construct the AI-CXR early warning system for KD. It will break through the current reliance on fever duration and subjective symptoms, providing support for early diagnosis and improving patient outcomes.

• Study Type: Observational
• Enrollment (Estimated): 20000

Contacts and Locations

• Study Contact: Name: Jian Wang, Doctoral Degree; Phone Number: 86-15900861356; Email: wangjian@xinhuemed.com.cn
• Study Contact Backup: Name: Bo Wang, PhD Candidate; Phone Number: 86-19921875669; Email: 18bowang@sjtu.edu.cn

Study Locations

• China
  • Shanghai Municipality
    • Shanghai, Shanghai Municipality, China, 2000000
      • Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

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.

Description

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)

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Case group; Inclusion criteria: (1) The age of seeking medical treatment is less than or equal to 18 years old; (2) The medical record system diagnosis contains the diagnosis of "Kawasaki Disease", "mucocutaneous lymph node syndrome" or "IVIG non-response Kawasaki disease". (3) At least one complete chest X-ray examination data (images and reports) is available during the same hospitalization. Exclusion criteria: (1) Chest X-ray quality issues: Severe artifacts, overexposure/underexposure leading to inability to assess key structures. (2) Incomplete clinical information, including lack of chest X-ray examination, laboratory tests, and unclear days of fever. (3) Inability to determine the final diagnosis (such as loss to follow-up, diagnosis in doubt).	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.
Control group; Inclusion criteria: (1) The age of seeking medical treatment is less than or equal to 18 years old; (2) The same period as the case group; (3) Fever lasts for 3 days or more; (4) Rule out the possibility of diagnosing Kawasaki disease Exclusion criteria: (1) Chest X-ray quality issues: Severe artifacts, overexposure/underexposure leading to inability to assess key structures. (2) Incomplete clinical information, including lack of chest X-ray examination, laboratory tests, and unclear days of fever. (3) Inability to make a clear final diagnosis (such as loss to follow-up, questionable diagnosis)	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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Time Frame
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

Collaborators and Investigators

• Sponsor: Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
• Collaborators: Hunan Provincial People's Hospital; Children's Hospital of Soochow University; Yangzhou No.1 People's Hospital; Women and Children Hospital of Qinghai Province
• Investigators: Study Chair: Kun Sun, Doctoral degree, Xinhua hospital affiliated with Shanghai Jiao Tong university school of medicine

Study record dates

Study Major Dates

• Study Start (Estimated): February 1, 2026
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): December 31, 2027

Study Registration Dates

• First Submitted: January 12, 2026
• First Submitted That Met QC Criteria: February 10, 2026
• First Posted (Actual): February 12, 2026

Study Record Updates

• Last Update Posted (Actual): February 12, 2026
• Last Update Submitted That Met QC Criteria: February 10, 2026
• Last Verified: December 1, 2025

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Kawasaki Disease; Mucocutaneous lymph node syndrome
• Additional Relevant MeSH Terms: Vascular Diseases; Cardiovascular Diseases; Skin Diseases; Lymphatic Diseases; Skin Diseases, Vascular; Vasculitis; Skin and Connective Tissue Diseases; Hemic and Lymphatic Diseases; Mucocutaneous Lymph Node Syndrome
• Other Study ID Numbers: XH-25-010

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: Due to confidentiality concerns, participant consent restrictions, and the need to comply with ethical and legal standards, Individual Participant Data (IPD) from this study will not be shared. The data contains sensitive health information that is protected by privacy regulations, and we do not have explicit consent from participants to share their data for secondary analysis. Additionally, institutional policies and data security requirements further restrict the release of IPD.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No