[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100652583":3},{"organization":4,"armGroups":7,"interventions":15,"overallOfficials":26,"centralContacts":31,"locations":37,"responsibleParty":51,"collaborators":10,"id":53,"slug":54,"hasResults":55,"nctId":56,"briefTitle":57,"officialTitle":58,"acronym":10,"eligibilityCriteria":59,"healthyVolunteers":60,"sex":61,"minAge":62,"maxAge":63,"enrollmentInfo":64,"targetDuration":67,"studyType":68,"phases":10,"briefSummary":69,"conditions":70,"keywords":72,"overallStatus":39,"whyStopped":10,"lastUpdateSubmitDate":78,"lastUpdatePostDateStruct":79,"startDateStruct":82,"completionDateStruct":84,"leadSponsor":86,"locationsCount":87},{"fullName":5,"class":6},"Marmara University","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"Root coverage surgery cohort",null,"Systemically healthy adults aged 18 to 65 years with Cairo RT1,RT2 pr RT3 gingival recessions, treated with a coronally advanced flap combined with a subepithelial connective tissue graft by a single operator and followed for six months. All participants received the same surgical technique; no comparison group was formed and no participant was assigned to a treatment for the purposes of this study. Standardised intraoral photographs and clinical measurements were obtained before surgery and at three and six months.",[13,14],"Procedure: Coronally advanced flap with subepithelial connective tissue graft","Diagnostic Test: Deep learning based prediction of root coverage outcome",[16,21],{"type":17,"name":18,"description":19,"armGroupLabels":20,"otherNames":10},"PROCEDURE","Coronally advanced flap with subepithelial connective tissue graft","A coronally advanced flap is raised over the recession defect and a subepithelial connective tissue graft harvested from the palate is positioned beneath it, after which the flap is sutured coronal to the cemento-enamel junction. Graft thickness, length and width are recorded for each treated site. The procedure was performed as routine clinical care and was not assigned for research purposes.",[9],{"type":22,"name":23,"description":24,"armGroupLabels":25,"otherNames":10},"DIAGNOSTIC_TEST","Deep learning based prediction of root coverage outcome","Preoperative intraoral photographs and baseline clinical variables are analysed by a deep learning model that predicts the outcome of root coverage surgery. The model output is not used in clinical decision making and does not influence treatment; it is compared retrospectively with the outcome measured by the treating periodontist at six months. The same photographs are also used to assign the recession type automatically, which is compared with the clinical assignment.",[9],[27],{"name":28,"affiliation":29,"role":30},"Leyla Kuru, Professor","Marmara University Faculty of Dentistry Department of Periodontology","PRINCIPAL_INVESTIGATOR",[32],{"name":33,"role":34,"phone":35,"phoneExt":10,"email":36},"Muhammed F Dogan, Resident","CONTACT","+905433890065","mfurkandogaan@gmail.com",[38],{"facility":29,"status":39,"city":40,"state":40,"zip":41,"country":42,"countryCode":10,"cosmosGeoPoint":43,"geoPoint":48,"contacts":49},"RECRUITING","Istanbul","34854","Turkey (Türkiye)",{"type":44,"coordinates":45},"Point",[46,47],28.94966,41.01384,{"lat":47,"lon":46},[50],{"name":33,"role":34,"phone":35,"phoneExt":10,"email":36},{"type":52,"investigatorFullName":10,"investigatorTitle":10,"investigatorAffiliation":10,"oldNameTitle":10,"oldOrganization":10},"SPONSOR","100652583","ai-based-prediction-of-root-coverage-outcome-from-intraoral-photographs-100652583",false,"NCT07775365","AI-Based Prediction of Root Coverage Outcome From Intraoral Photographs","Development and Internal Validation of a Deep Learning Model Predicting the Outcome of Root Coverage Surgery From Preoperative Intraoral Photographs: A Prospective Observational Cohort Study","Inclusion Criteria:\n\n* Systemically healthy patients (ASA I or II status) with no contraindications for periodontal surgery.\n* Adult patients aged 18 to 65 years.\n* Presence of isolated or multiple gingival recessions classified as Cairo RT1, RT2 or RT3 in the maxilla or mandible.\n* Patients with good oral hygiene standards, defined as a Full Mouth Plaque Score (FMPS) and Full Mouth Bleeding Score (FMBS) of \\\u003C 20% at baseline.\n* Presence of an identifiable Cemento-Enamel Junction (CEJ) (Crucial for AI segmentation).\n\nExclusion Criteria:\n\n* Patients with uncontrolled diabetes, immune system disorders, or pregnant\u002Flactating women.\n* Teeth with cervical restorations or abrasions that obscure the CEJ.\n* Malpositioned or rotated teeth that would distort the photographic angle for AI analysis.",true,"ALL","18 Years","65 Years",{"count":65,"type":66},36,"ESTIMATED","6 Months","OBSERVATIONAL","This study evaluates whether the outcome of root coverage surgery can be predicted from a preoperative intraoral photograph. Adults with Cairo RT1,RT2 or RT3 gingival recessions treated with a coronally advanced flap and a connective tissue graft are followed for six months. Standardised photographs and clinical measurements are obtained before surgery and at each follow-up visit. A deep learning model is developed to predict the surgical outcome from the preoperative photograph and baseline clinical variables, and its performance is compared with the outcome measured clinically at six months. The model does not influence treatment decisions.",[71],"Gingival Recessions",[73,74,75,76,77],"Gingival Recession","Artificial Intelligence","Image Analysis","Deep Learning","prognostic model","2026-08-16",{"date":80,"type":81},"2026-08-20","ACTUAL",{"date":83,"type":81},"2025-09-17",{"date":85,"type":66},"2027-09-17",{"name":5,"class":6},1]