[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100624132":3},{"organization":4,"armGroups":7,"interventions":18,"overallOfficials":24,"centralContacts":29,"locations":39,"responsibleParty":53,"collaborators":55,"id":64,"slug":65,"hasResults":66,"nctId":67,"briefTitle":68,"officialTitle":68,"acronym":10,"eligibilityCriteria":69,"healthyVolunteers":70,"sex":71,"minAge":72,"maxAge":73,"enrollmentInfo":74,"targetDuration":10,"studyType":77,"phases":10,"briefSummary":78,"conditions":79,"keywords":83,"overallStatus":86,"whyStopped":10,"lastUpdateSubmitDate":87,"lastUpdatePostDateStruct":88,"startDateStruct":91,"completionDateStruct":93,"leadSponsor":95,"locationsCount":96},{"fullName":5,"class":6},"Xinhua Hospital, Shanghai Jiao Tong University School of Medicine","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"Case group",null,"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.\n\nExclusion criteria: (1) Chest X-ray quality issues: Severe artifacts, overexposure\u002Funderexposure 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).",[13],"Diagnostic Test: AI-Based Early Warning System for Kawasaki Disease",{"label":15,"type":10,"description":16,"interventionNames":17},"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\u002Funderexposure 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)",[13],[19],{"type":20,"name":21,"description":22,"armGroupLabels":23,"otherNames":10},"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.",[9,15],[25],{"name":26,"affiliation":27,"role":28},"Kun Sun, Doctoral degree","Xinhua hospital affiliated with Shanghai Jiao Tong university school of medicine","STUDY_CHAIR",[30,35],{"name":31,"role":32,"phone":33,"phoneExt":10,"email":34},"Jian Wang, Doctoral Degree","CONTACT","86-15900861356","wangjian@xinhuemed.com.cn",{"name":36,"role":32,"phone":37,"phoneExt":10,"email":38},"Bo Wang, PhD Candidate","86-19921875669","18bowang@sjtu.edu.cn",[40],{"facility":41,"status":10,"city":42,"state":43,"zip":44,"country":45,"countryCode":46,"cosmosGeoPoint":47,"geoPoint":52,"contacts":10},"Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine","Shanghai","Shanghai Municipality","2000000","China","CN",{"type":48,"coordinates":49},"Point",[50,51],121.45806,31.22222,{"lat":51,"lon":50},{"type":54,"investigatorFullName":10,"investigatorTitle":10,"investigatorAffiliation":10,"oldNameTitle":10,"oldOrganization":10},"SPONSOR",[56,58,60,62],{"name":57,"class":6},"Children's Hospital of Soochow University",{"name":59,"class":6},"Hunan Provincial People's Hospital",{"name":61,"class":6},"Women and Children Hospital of Qinghai Province",{"name":63,"class":6},"Yangzhou No.1 People's Hospital","100624132","clinical-study-on-an-artificial-intelligence-assisted-chest-radiograph-model-based-on-big-data-and-deep-learning-for-early-detection-of-kawasaki-disease-100624132",false,"NCT07405658","Clinical Study on an Artificial Intelligence-Assisted Chest Radiograph Model Based on Big Data and Deep Learning for Early Detection of Kawasaki Disease","Inclusion Criteria:\n\n1. Case group\n\n   * 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\"\n   * At least one complete chest X-ray examination data (images and reports) is available during the same hospitalization\n2. Control group\n\n   * The age of seeking medical treatment is less than or equal to 18 years old\n   * The same period as the case group\n   * Fever lasts for 3 days or more\n   * Rule out the possibility of diagnosing Kawasaki disease\n\nExclusion Criteria:\n\n1. Case group\n\n   * Chest X-ray quality issues: Severe artifacts, overexposure\u002Funderexposure leading to inability to assess key structures\n   * 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)\n2. Control group\n\n   * Chest X-ray quality issues: Severe artifacts, overexposure\u002Funderexposure leading to inability to assess key structures\n   * Incomplete clinical information, including lack of chest X-ray examination, laboratory tests, and unclear days of fever\n   * Inability to make a clear final diagnosis (such as loss to follow-up, questionable diagnosis)",true,"ALL","0 Years","18 Years",{"count":75,"type":76},20000,"ESTIMATED","OBSERVATIONAL","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:\n\n1. Can AI modeling of CXR features help identify high-risk KD patients earlier than current diagnostic methods?\n2. Can the AI system predict the optimal IVIG treatment window and coronary artery risks in KD patients?\n\nParticipants will:\n\nProvide 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",[80,81,82],"Kawasaki Disease","Chest X-ray for Clinical Evaluation","Mucocutaneous Lymph Node Syndrome",[80,84,85],"Artificial Intelligence","Mucocutaneous lymph node syndrome","NOT_YET_RECRUITING","2026-02-10",{"date":89,"type":90},"2026-02-12","ACTUAL",{"date":92,"type":76},"2026-02-01",{"date":94,"type":76},"2027-12-31",{"name":5,"class":6},1]