[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"mucocutaneous-lymph-node-syndrome\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:mucocutaneous-lymph-node-syndrome":42},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,1,0,[8],{"id":9,"slug":4,"hasResults":10,"nctId":11,"briefTitle":12,"officialTitle":12,"acronym":4,"eligibilityCriteria":13,"healthyVolunteers":14,"sex":15,"minAge":16,"maxAge":17,"enrollmentInfo":18,"targetDuration":4,"studyType":21,"phases":4,"briefSummary":22,"conditions":23,"keywords":27,"overallStatus":30,"whyStopped":4,"lastUpdateSubmitDate":31,"lastUpdatePostDateStruct":32,"startDateStruct":35,"completionDateStruct":37,"leadSponsor":39,"locationsCount":5},"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":19,"type":20},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",[24,25,26],"Kawasaki Disease","Chest X-ray for Clinical Evaluation","Mucocutaneous Lymph Node Syndrome",[24,28,29],"Artificial Intelligence","Mucocutaneous lymph node syndrome","NOT_YET_RECRUITING","2026-02-10",{"date":33,"type":34},"2026-02-12","ACTUAL",{"date":36,"type":20},"2026-02-01",{"date":38,"type":20},"2027-12-31",{"name":40,"class":41},"Xinhua Hospital, Shanghai Jiao Tong University School of Medicine","OTHER",""]