[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100586675":3},{"organization":4,"outcomesModule":7,"designInfo":19,"detailedDescription":18,"studyPopulation":22,"armGroups":18,"interventions":23,"overallOfficials":18,"centralContacts":18,"locations":18,"responsibleParty":28,"collaborators":18,"id":32,"slug":18,"hasResults":33,"nctId":34,"briefTitle":35,"officialTitle":35,"acronym":18,"eligibilityCriteria":36,"healthyVolunteers":37,"sex":38,"minAge":18,"maxAge":39,"enrollmentInfo":40,"targetDuration":18,"studyType":43,"phases":18,"briefSummary":44,"conditions":45,"keywords":18,"overallStatus":47,"whyStopped":18,"lastUpdateSubmitDate":48,"lastUpdatePostDateStruct":49,"startDateStruct":52,"completionDateStruct":54,"leadSponsor":56,"locationsCount":18},{"fullName":5,"class":6},"Kunming Children's Hospital","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":18},[9],{"measure":10,"description":11,"timeFrame":12},"To evaluate the coincidence rate between doctors' independent diagnosis and the diagnosis recommended by the AI system.","The reference standard is the EGG interpreted by 3 clinicians who had attended the uniformly training program and had more than 5 years of experience in diagnosing epilepsy in children.","Within 48 hours after the completion of EEG monitoring",[14],{"measure":15,"description":16,"timeFrame":17},"To evaluate the diagnostic efficiency of clinicians at two-stage","The time taken by physicians to interpret EEG independently and with the aid of AI was measured.","Immediately after the end of EEG interpretation",null,{"allocation":18,"interventionModel":18,"interventionModelDescription":18,"primaryPurpose":18,"observationalModel":20,"timePerspective":21,"maskingInfo":18},"COHORT","PROSPECTIVE","children aged \\\u003C 18 years old with suspected epilepsy who need EEG examination",[24],{"type":25,"name":26,"description":27,"armGroupLabels":18,"otherNames":18},"DIAGNOSTIC_TEST","AI EEG analysis system","The first stage, physicians independently diagnose pediatric epilepsy with EEG; the second stage, Artificial intelligence EEG analysis system assisted physicians to diagnose pediatric epilepsy",{"type":29,"investigatorFullName":30,"investigatorTitle":31,"investigatorAffiliation":5,"oldNameTitle":18,"oldOrganization":18},"PRINCIPAL_INVESTIGATOR","Xiaomei Liu","Professor","100586675",false,"NCT06918457","A Clinical Trial With a Self-controlled, Multicenter, Pediatric EEG Intelligent Analysis System to Assist in Diagnosis","Inclusion Criteria:\n\n* Age \\\u003C 18 years old\n* Children with suspected epilepsy\n\nExclusion Criteria:\n\n* During EEG monitoring, the patients had other serious neurological diseases and mental diseases concurrently\n* Used medication that affect EEG data within 3 weeks, such as sedatives and anti-epileptic medications\n* Substandard data quality, such as data lack of key records, electrode connection discontinuity, insufficient recording time, or the presence of serious artifacts\n* Incomplete or missing data\n* Equipment or operational abnormalities, data for which EEG monitoring has not been performed continuously",true,"ALL","18 Years",{"count":41,"type":42},1000,"ESTIMATED","OBSERVATIONAL","A diagnostic accuracy study on Artificial intelligence EEG analysis system assisted doctors to diagnose pediatric epilepsy.",[46],"Pediatric Epilepsy","NOT_YET_RECRUITING","2025-04-06",{"date":50,"type":51},"2025-04-09","ACTUAL",{"date":53,"type":42},"2025-06-03",{"date":55,"type":42},"2026-07-01",{"name":5,"class":6}]