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

• 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.
• 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.↑

• 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.

• 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.

• 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.

Inclusion criteria

• 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

Exclusion criteria

• 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)