[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100567422":3},{"organization":4,"armGroups":7,"interventions":8,"overallOfficials":12,"centralContacts":23,"locations":7,"responsibleParty":29,"collaborators":7,"id":33,"slug":34,"hasResults":35,"nctId":36,"briefTitle":37,"officialTitle":38,"acronym":7,"eligibilityCriteria":39,"healthyVolunteers":40,"sex":41,"minAge":42,"maxAge":43,"enrollmentInfo":44,"targetDuration":47,"studyType":48,"phases":7,"briefSummary":49,"conditions":50,"keywords":52,"overallStatus":54,"whyStopped":7,"lastUpdateSubmitDate":55,"lastUpdatePostDateStruct":56,"startDateStruct":59,"completionDateStruct":61,"leadSponsor":63,"locationsCount":7},{"fullName":5,"class":6},"Cairo University","OTHER",null,[9],{"type":6,"name":10,"description":11,"armGroupLabels":7,"otherNames":7},"artificial intelligence models (YOLO and Mask-RCNN)","machine learning model will used to detect secondary caries around restorations by comparing the results with digital bitewing radiography",[13,17,20],{"name":14,"affiliation":15,"role":16},"Prof. Dr. Heba Hamza, professor","Professor of Conservative Dentistry Department, Faculty of Dentistry, Cairo University","STUDY_DIRECTOR",{"name":18,"affiliation":19,"role":16},"Dr. Rawda Hisham A. ElAziz, lecturer","Lecturer of Conservative Dentistry Department, Faculty of Dentistry, Cairo University",{"name":21,"affiliation":22,"role":16},"Dr. Asmaa Ahmed Elsayed Osman, lecturer","Lecturer of Information Technology, Faculty of Computers and Artificial Intelligence, Cairo University",[24],{"name":25,"role":26,"phone":27,"phoneExt":7,"email":28},"Heba-Tullah mohamed mansour, master","CONTACT","01025457570","hebatullah.mansour@dentistry.cu.edu.eg",{"type":30,"investigatorFullName":31,"investigatorTitle":32,"investigatorAffiliation":5,"oldNameTitle":7,"oldOrganization":7},"PRINCIPAL_INVESTIGATOR","Heba Tallah Mohamed Mansour","general Practitioner at Health Administration, Faculty of Pharmacy, Cairo University","100567422","artificial-intellegence-rivals-digital-bitewing-in-detect-secondary-caries-100567422",false,"NCT06667986","Artificial Intellegence Rivals Digital Bitewing in Detect Secondary Caries","AI Rivals Traditional Bite Wing Radiography in Detecting Proximal Secondary Caries in A Group of Egyptian Patients at Cairo University, Faculty OF Dentistry Hospital (Diagnostic Accuracy Study)","Inclusion Criteria:\n\n1. Adult Patients Aged 22-60 Patient\n2. Males or females.\n3. Patients have proximal restorations.\n4. Co-operative patients who show interest in participating in the study.\n\nExclusion Criteria:\n\n1. Patients with orthodontic appliances, or bridge work that might interfere with evaluation\n2. Patients with no caries.\n3. Systematic disease that may affect participation.\n4. Patients not willing to be part of the study or ones who refuse to sign the informed consent.",true,"ALL","22 Years","60 Years",{"count":45,"type":46},322,"ESTIMATED","1 Year","OBSERVATIONAL","This study uses digital bitewing radiography as a standard for diagnosing proximal secondary caries. Patients will undergo imaging with a parallel technique and fixed settings to ensure high-quality, consistent images. Radiographs are interpreted by experienced dental professionals to maintain diagnostic accuracy. Machine learning models YOLO and Mask-RCNN will analyze these images in three phases: pre-analytical, analytical, and post-analytical. A dataset of 322 labeled images, annotated by experts, is used to train these models. Data augmentation methods enhance model performance, and accuracy is assessed against radiographic results to confirm reliability.",[51],"Caries,Dental",[53],"Secondary caries, Artificial intelligence, Digital bitewing radiography, Diagnostic accuracy study.","NOT_YET_RECRUITING","2024-10-30",{"date":57,"type":58},"2024-10-31","ACTUAL",{"date":60,"type":46},"2024-11-15",{"date":62,"type":46},"2026-02-15",{"name":5,"class":6}]