Clinical Study on an Artificial Intelligence-Assisted Chest Radiograph Model Based on Big Data and Deep Learning for Early Detection of Kawasaki Disease
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 20,000
- 试验地点
- 1
- 主要终点
- Area Under Curve
研究概览
简要总结
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:
- Can AI modeling of CXR features help identify high-risk KD patients earlier than current diagnostic methods?
- 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
详细描述
- 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.↑ 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.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 0 Years 至 18 Years（Child, Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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
排除标准
- •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)
研究组 & 干预措施
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).
干预措施: AI-Based Early Warning System for Kawasaki Disease (Diagnostic Test)
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)
干预措施: AI-Based Early Warning System for Kawasaki Disease (Diagnostic Test)
结局指标
主要结局
Area Under Curve
时间窗: Up to 14 days after fever onset
sensitivity
时间窗: Up to 14 days after fever onset
specificity
时间窗: Up to 14 days after fever onset
次要结局
未报告次要终点