[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100573704":3},{"organization":4,"armGroups":7,"interventions":18,"overallOfficials":10,"centralContacts":10,"locations":26,"responsibleParty":48,"collaborators":10,"id":52,"slug":53,"hasResults":54,"nctId":55,"briefTitle":56,"officialTitle":57,"acronym":10,"eligibilityCriteria":58,"healthyVolunteers":54,"sex":59,"minAge":60,"maxAge":61,"enrollmentInfo":62,"targetDuration":10,"studyType":65,"phases":10,"briefSummary":66,"conditions":67,"keywords":71,"overallStatus":76,"whyStopped":10,"lastUpdateSubmitDate":77,"lastUpdatePostDateStruct":78,"startDateStruct":81,"completionDateStruct":83,"leadSponsor":85,"locationsCount":86},{"fullName":5,"class":6},"Cairo University","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"training group",null,"images used to train the AI models on detection of dental caries from intraoral images.",[13],"Diagnostic Test: FASTER RCNN",{"label":15,"type":10,"description":16,"interventionNames":17},"test group","images used to test the accuracy of the AI models in diagnosis of dental caries from intraoral images.",[13],[19],{"type":20,"name":21,"description":22,"armGroupLabels":23,"otherNames":24},"DIAGNOSTIC_TEST","FASTER RCNN","train artificial intelligence models ( FASTER RCNN, YOLOY ) to detect dental caries , then test their accuracy",[15,9],[25],"YOLO",[27],{"facility":28,"status":10,"city":29,"state":30,"zip":10,"country":31,"countryCode":32,"cosmosGeoPoint":33,"geoPoint":38,"contacts":39},"Cairo university","Giza","Giza Governorate","Egypt","EG",{"type":34,"coordinates":35},"Point",[36,37],31.20861,30.00944,{"lat":37,"lon":36},[40,45],{"name":41,"role":42,"phone":43,"phoneExt":43,"email":44},"mahmoud ahmed Vice President for Graduate Studies and Research, Phd","CONTACT","0235674835","info@cu.edu.eg",{"name":46,"role":47,"phone":10,"phoneExt":10,"email":10},"sama sayed hanafy, Phd","SUB_INVESTIGATOR",{"type":49,"investigatorFullName":50,"investigatorTitle":51,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"PRINCIPAL_INVESTIGATOR","Sherine Tarek Mohamed Elsayed Khaled","principal investigator","100573704","accuracy-of-detection-of-dental-caries-from-intraoral-images-using-different-artificiai-intelligence-models-100573704",false,"NCT06749743","Accuracy Of Detection Of Dental Caries From Intraoral Images Using Different ArtificiaI Intelligence Models","Accuracy Of Dental Caries Detection From Intraoral Images Using Different Artificial Intelligence Models Versus Conventional Visual Examination Among A Group Of Children: A Diagnostic Accuracy Study","Inclusion Criteria:\n\n* Child dentition having at least one decayed tooth.\n\nExclusion Criteria:\n\n* Child dentition with developmental enamel defects.\n* Children with any systemic medical condition.\n* Parent \u002F child refuse to participate in the study.\n* Uncooperative child.","ALL","4 Years","12 Years",{"count":63,"type":64},398,"ESTIMATED","OBSERVATIONAL","The goal of this observational study is to evaluate the diagnostic accuracy of different deep learning models in detecting dental caries from intra oral images taken by a professional intra oral camera in children. The main question it aims to answer is:\n\nWhat is the diagnostic accuracy of different deep learning models in detecting dental caries from intra oral images taken by a professional intra oral camera in children compared to the conventional clinical visual examination?",[68,69,70],"Dental Caries (Diagnosis)","Artifical Intelligence","Intraoral Images",[72,73,74,75],"artificial intelligence","dental caries","diagnosis","intraoral images","NOT_YET_RECRUITING","2025-02-28",{"date":79,"type":80},"2025-03-04","ACTUAL",{"date":82,"type":64},"2025-04-30",{"date":84,"type":64},"2025-12-30",{"name":5,"class":6},1]