[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100651301":3},{"organization":4,"armGroups":7,"interventions":13,"overallOfficials":10,"centralContacts":19,"locations":25,"responsibleParty":40,"collaborators":10,"id":42,"slug":43,"hasResults":44,"nctId":45,"briefTitle":46,"officialTitle":46,"acronym":10,"eligibilityCriteria":47,"healthyVolunteers":48,"sex":49,"minAge":50,"maxAge":10,"enrollmentInfo":51,"targetDuration":10,"studyType":54,"phases":10,"briefSummary":55,"conditions":56,"keywords":10,"overallStatus":28,"whyStopped":10,"lastUpdateSubmitDate":58,"lastUpdatePostDateStruct":59,"startDateStruct":62,"completionDateStruct":64,"leadSponsor":66,"locationsCount":67},{"fullName":5,"class":6},"Democritus University of Thrace","OTHER",[8],{"label":9,"type":10,"description":10,"interventionNames":11},"Participants from outpatient clinics",null,[12],"Diagnostic Test: Color retinal fundus photograph",[14],{"type":15,"name":16,"description":17,"armGroupLabels":18,"otherNames":10},"DIAGNOSTIC_TEST","Color retinal fundus photograph","Diagnostic Test: color fundus photograph Description: Color retinal fundus photographs will be acquired from: Digital non-mydriatic fundus cameras.",[9],[20],{"name":21,"role":22,"phone":23,"phoneExt":10,"email":24},"Georgios Labiris","CONTACT","+302551030990","glampiri@med.duth.gr",[26],{"facility":27,"status":28,"city":29,"state":10,"zip":10,"country":30,"countryCode":31,"cosmosGeoPoint":32,"geoPoint":37,"contacts":38},"University Hospital of Alexandroupolis","RECRUITING","Alexandroupoli","Greece","GR",{"type":33,"coordinates":34},"Point",[35,36],25.87644,40.84995,{"lat":36,"lon":35},[39],{"name":21,"role":22,"phone":23,"phoneExt":10,"email":24},{"type":41,"investigatorFullName":21,"investigatorTitle":5,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"PRINCIPAL_INVESTIGATOR","100651301","validation-of-the-artificial-intelligence-subsystem-of-the-ddart-medical-device-for-the-automated-detection-of-lesions-compatible-with-diabetic-retinopathy-in-a-random-sample-of-retinal-fundus-photographs-100651301",false,"NCT07758582","Validation of the Artificial Intelligence Subsystem of the DDART Medical Device for the Automated Detection of Lesions Compatible With Diabetic Retinopathy in a Random Sample of Retinal Fundus Photographs","Inclusion Criteria Age ≥18 years Availability of a high-resolution color fundus photograph Confirmed diagnosis established by an ophthalmology specialist Exclusion Criteria Poor-quality retinal images Concomitant ocular diseases that interfere with image interpretation",true,"ALL","18 Years",{"count":52,"type":53},2000,"ESTIMATED","OBSERVATIONAL","To validate the artificial intelligence subsystem of the DDART medical device for the automated detection of lesions compatible with diabetic retinopathy in a random sample of retinal fundus photographs.\n\nSecondary Objectives To determine the sensitivity and specificity of the artificial intelligence subsystem for the detection of diabetic retinopathy.\n\nTo estimate the overall diagnostic accuracy and the area under the receiver operating characteristic (ROC) curve (AUC).\n\nTo compare the performance of the algorithm with that of experienced ophthalmologists.\n\nTo evaluate the ability of the model to distinguish between different stages of disease severity",[57],"Diabetic Retinopathy","2026-08-06",{"date":60,"type":61},"2026-08-11","ACTUAL",{"date":63,"type":61},"2026-05-21",{"date":65,"type":53},"2027-04-19",{"name":5,"class":6},1]