Kawasaki DiseaseNCT07405658
Clinical Study on an Artificial Intelligence-Assisted Chest Radiograph Model Based on Big Data and Deep Learning for Early Detection of Kawasaki Disease
An observational study of Kawasaki Disease and Chest X-ray for Clinical Evaluation, run by Xinhua Hospital, Shanghai Jiao Tong University School of Medicine at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 0 to 18 yearsHealthy volunteers can take part
- Conditions the study is for
- Kawasaki Disease, Chest X-ray for Clinical Evaluation, Mucocutaneous Lymph Node Syndrome
- What is being tested?
- AI-Based Early Warning System for Kawasaki Disease
- How many people
- 20,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Feb 2026to 31 Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 0 to 18 years
- All sexes
- Healthy volunteers accepted
- Child, Adult
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.
Who may be able to join
- 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
Who cannot join
- 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)
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site
Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine
Shanghai, Shanghai Municipality, 2000000
About this study
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?
Read more
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
Read the detailed description
- 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.
- Being tested
- AI-Based Early Warning System for Kawasaki Disease
- Keywords
- Kawasaki Disease, Artificial Intelligence, Mucocutaneous lymph node syndrome
- Medical subject headings
- Mucocutaneous Lymph Node Syndrome
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Based Early Warning System for Kawasaki DiseaseDiagnostic test
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 group | Role | What they receive |
|---|---|---|
| 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). Receives: Diagnostic Test: AI-Based Early Warning System for Kawasaki Disease | |
| 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) Receives: Diagnostic Test: AI-Based Early Warning System for Kawasaki Disease |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area Under Curve
Measured: Up to 14 days after fever onset
sensitivity
Measured: Up to 14 days after fever onset
specificity
Measured: Up to 14 days after fever onset
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Feb 2026
- Study start1 Feb 2026 (estimated)
- Primary completion31 Dec 2026 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Kawasaki DiseaseStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 20,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
- Sponsor type
- University, hospital or other
- Collaborators
- Children's Hospital of Soochow University, Hunan Provincial People's Hospital, Women and Children Hospital of Qinghai Province, Yangzhou No.1 People's Hospital
- Responsible party
- Sponsor
- Investigators
- Kun Sun, Doctoral degree, Study chair, Xinhua hospital affiliated with Shanghai Jiao Tong university school of medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07405658
- Sponsor's protocol ID
- XH-25-010
- Status last verified
- Dec 2025
- First submitted
- 12 Jan 2026
- Last updated
- 12 Feb 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: 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.
Study at a glance
From the registry record, last updated by the sponsor on 12 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Clinical Study on an Artificial Intelligence-Assisted Chest Radiograph Model Based on Big Data and Deep Learning for Early Detection of Kawasaki Disease
- ClinicalTrials.gov ID
- NCT07405658
- Acronym / sponsor ID
- XH-25-010
- Keywords
- Kawasaki Disease, Artificial Intelligence, Mucocutaneous lymph node syndrome
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 20,000 participants (estimated)
- Lead sponsor
- Xinhua Hospital, Shanghai Jiao Tong University School of MedicineUniversity, hospital or other
- Collaborators
- Children's Hospital of Soochow University, Hunan Provincial People's Hospital, Women and Children Hospital of Qinghai Province, Yangzhou No.1 People's Hospital
- Study start
- 1 Feb 2026 (estimated)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 12 Feb 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
Detailed description
- 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.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Case group | – | Diagnostic Test: AI-Based Early Warning System for Kawasaki DiseaseInclusion 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). |
| Control group | – | Diagnostic Test: AI-Based Early Warning System for Kawasaki DiseaseInclusion 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) |
Who may qualify
- Ages
- 0 to 18 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult
- 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.
Key inclusion criteria (8 listed)
- Case group
- Control group
Key exclusion criteria (7 listed)
- Case group
- Control group
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-Based Early Warning System for Kawasaki DiseaseThis 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. | Diagnostic test | Case group, Control group |
Primary outcome
Area Under Curve
Time frame: Up to 14 days after fever onset
sensitivity
Time frame: Up to 14 days after fever onset
specificity
Time frame: Up to 14 days after fever onset
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Feb 2026.
- Individual participant dataSharing planned: No. 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Kun Sun, Doctoral degree study chair
Xinhua hospital affiliated with Shanghai Jiao Tong university school of medicine
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Kawasaki Disease and Chest X-ray for Clinical Evaluation, run by Xinhua Hospital, Shanghai Jiao Tong University School of Medicine at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 8 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 12 Feb 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Jian Wang, Doctoral Degree (86-15900861356, wangjian@xinhuemed.com.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area Under Curve (time frame: Up to 14 days after fever onset). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 12 Feb 2026.