[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100631807":3},{"organization":4,"outcomesModule":7,"designInfo":18,"detailedDescription":17,"studyPopulation":17,"armGroups":24,"interventions":34,"overallOfficials":40,"centralContacts":47,"locations":56,"responsibleParty":81,"collaborators":17,"id":83,"slug":17,"hasResults":84,"nctId":85,"briefTitle":86,"officialTitle":87,"acronym":88,"eligibilityCriteria":89,"healthyVolunteers":90,"sex":91,"minAge":92,"maxAge":93,"enrollmentInfo":94,"targetDuration":17,"studyType":97,"phases":98,"briefSummary":100,"conditions":101,"keywords":17,"overallStatus":58,"whyStopped":17,"lastUpdateSubmitDate":104,"lastUpdatePostDateStruct":105,"startDateStruct":108,"completionDateStruct":110,"leadSponsor":112,"locationsCount":113},{"fullName":5,"class":6},"University of Bari Aldo Moro","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":17},[9],{"measure":10,"description":11,"timeFrame":12},"Time required for CBCT interpretation (minutes)","assessment of the time required for CBCT interpretation by the surgeon. A digital stopwatch was used to record the operative time required for each procedural step, with measurements expressed in seconds, in order to obtain an objective and standardized assessment of execution time.","Day 1",[14],{"measure":15,"description":16,"timeFrame":12},"Intraoperative and Postoperative Complications","* Unexpected endodontic treatment of adjacent teeth\n* Intraoperative nerve exposure\n* Paresthesia\n* Excessive bone removal\n* Incomplete lesion removal\n* Postoperative infection\n* Delayed healing\n* Sinus involvement\n* Root damage to adjacent teeth",null,{"allocation":19,"interventionModel":20,"interventionModelDescription":17,"primaryPurpose":21,"observationalModel":17,"timePerspective":17,"maskingInfo":22},"RANDOMIZED","PARALLEL","DIAGNOSTIC",{"masking":23,"maskingDescription":17,"whoMasked":17},"NONE",[25,28],{"label":26,"type":27,"description":17,"interventionNames":17},"Patients with endosseous lesion-Analyzed using conventional CBCT","NO_INTERVENTION",{"label":29,"type":30,"description":31,"interventionNames":32},"Patients with endosseous lesion- AI-assisted evaluation","EXPERIMENTAL","* Automated segmentation of the lesion\n* 3D reconstruction\n* Volumetric calculation",[33],"Diagnostic Test: AI assisted Evaluation",[35],{"type":36,"name":37,"description":38,"armGroupLabels":39,"otherNames":17},"DIAGNOSTIC_TEST","AI assisted Evaluation","CBCT scans were processed using AI-based software capable of:\n\n* Automated segmentation of the lesion\n* 3D reconstruction\n* Volumetric calculation",[29],[41,44],{"name":42,"affiliation":5,"role":43},"Giuseppe D'Albis, Dr","PRINCIPAL_INVESTIGATOR",{"name":45,"affiliation":5,"role":46},"Saverio Capodiferro, Prof","STUDY_DIRECTOR",[48,53],{"name":49,"role":50,"phone":51,"phoneExt":17,"email":52},"Giuseppe D'Albis, Dr.","CONTACT","+393495103642","giuseppe.dalbis@uniba.it",{"name":54,"role":50,"phone":17,"phoneExt":17,"email":55},"Saverio Capodiferro, Prof.","saverio.capodiferro@uniba.it",[57,73],{"facility":5,"status":58,"city":59,"state":17,"zip":60,"country":61,"countryCode":62,"cosmosGeoPoint":63,"geoPoint":68,"contacts":69},"RECRUITING","Bari","70021","Italy","IT",{"type":64,"coordinates":65},"Point",[66,67],16.86982,41.12066,{"lat":67,"lon":66},[70],{"name":71,"role":50,"phone":51,"phoneExt":17,"email":72},"Giuseppe D'Albis","dalbisgiuseppe@hotmail.com",{"facility":74,"status":58,"city":59,"state":17,"zip":75,"country":61,"countryCode":62,"cosmosGeoPoint":76,"geoPoint":78,"contacts":79},"Dr. Giuseppe D'Albis","70124",{"type":64,"coordinates":77},[66,67],{"lat":67,"lon":66},[80],{"name":49,"role":50,"phone":51,"phoneExt":17,"email":52},{"type":43,"investigatorFullName":71,"investigatorTitle":82,"investigatorAffiliation":5,"oldNameTitle":17,"oldOrganization":17},"Principal Investigator","100631807",false,"NCT07505485","Artificial Intelligence-Based Assessment of Endosseous Lesions","Artificial Intelligence-Based Assessment of Endosseous Lesions: A Prospective Clinical Study","AIpreop","Inclusion Criteria:\n\n* Good health according to the System of the American Society of Anesthesiology\n* Aged older than 18 years\n* No general medical contraindication for surgery\n\nExclusion Criteria:\n\n* Smoking more than 15 cigarettes a day\n\n  * Pregnancy\n  * Acute infections",true,"ALL","18 Years","80 Years",{"count":95,"type":96},10,"ESTIMATED","INTERVENTIONAL",[99],"NA","Despite these advances, CBCT interpretation remains largely qualitative and dependent on the clinician's experience. Conventional evaluation is based on two-dimensional slices and linear measurements, which may underestimate lesion complexity and spatial distribution.\n\nRecent developments in Artificial Intelligence in Medicine have introduced automated image segmentation tools capable of identifying lesion boundaries and calculating volumetric data. These technologies allow a transition from subjective assessment to objective, reproducible quantification.\n\nThe potential clinical advantages include:\n\n* Objective measurement of lesion size (volume in mm³)\n* Improved surgical planning\n* Enhanced prediction of anatomical involvement\n* Reduction of diagnostic errors\n* Standardization of follow-up and outcome assessment Therefore, the aim of the present study was to evaluate the clinical impact of AI-based segmentation and volumetric analysis of endosseous lesions compared to conventional CBCT interpretation.",[102,103],"Maxillary Cyst","Mandibular Cyst","2026-04-12",{"date":106,"type":107},"2026-04-15","ACTUAL",{"date":109,"type":96},"2026-04-01",{"date":111,"type":96},"2026-05-01",{"name":5,"class":6},2]