[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100622079":3},{"organization":4,"armGroups":7,"interventions":19,"overallOfficials":18,"centralContacts":25,"locations":31,"responsibleParty":39,"collaborators":18,"id":41,"slug":18,"hasResults":42,"nctId":43,"briefTitle":44,"officialTitle":45,"acronym":46,"eligibilityCriteria":47,"healthyVolunteers":42,"sex":48,"minAge":49,"maxAge":18,"enrollmentInfo":50,"targetDuration":18,"studyType":53,"phases":54,"briefSummary":56,"conditions":57,"keywords":18,"overallStatus":34,"whyStopped":18,"lastUpdateSubmitDate":61,"lastUpdatePostDateStruct":62,"startDateStruct":65,"completionDateStruct":67,"leadSponsor":69,"locationsCount":70},{"fullName":5,"class":6},"VUNO Inc.","INDUSTRY",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"Intervention group","EXPERIMENTAL","For participants assigned to the intervention group, VUNO Med®-Fundus AI™ will be applied to the acquired fundus images, and the AI-generated outputs will be shown to clinicians during routine care.",[13],"Device: VUNO Med®-Fundus AI™",{"label":15,"type":16,"description":17,"interventionNames":18},"Control group","NO_INTERVENTION","For participants assigned to the control group, fundus images will be interpreted according to usual clinical care without AI assistance.",null,[20],{"type":21,"name":22,"description":23,"armGroupLabels":24,"otherNames":18},"DEVICE","VUNO Med®-Fundus AI™","VUNO Med®-Fundus AI™ is an artificial intelligence-based fundus image detection and diagnostic support software. The software automatically identifies abnormal retinal findings and provides information on the type and location of detected abnormalities to aid clinical decision-making.",[9],[26],{"name":27,"role":28,"phone":29,"phoneExt":18,"email":30},"Hee Jun Park","CONTACT","82-10-7101-2844","heejun.park@vuno.co",[32],{"facility":33,"status":34,"city":35,"state":36,"zip":37,"country":38,"cosmosGeoPoint":18,"geoPoint":18,"contacts":18},"Inha University Hospital","RECRUITING","Incheon","Gyeonggi-do","22332","South Korea",{"type":40,"investigatorFullName":18,"investigatorTitle":18,"investigatorAffiliation":18,"oldNameTitle":18,"oldOrganization":18},"SPONSOR","100622079",false,"NCT07378956","Clinical Efficacy of Implementing an AI-SaMD for Funduscopy Analysis in Patients With Diabetes Mellitus","Clinical Efficacy of Implementing an AI-Driven Software as a Medical Device (SaMD) for Funduscopy Analysis in Patients With Diabetes Mellitus: A Randomized Controlled Trial Protocol","SAFE-DM","Inclusion Criteria:\n\n* Adults aged 19 years or older.\n* A documented diagnosis of type 2 diabetes mellitus.\n* Ability to communicate adequately and provide written informed consent for participation in the study.\n\nExclusion Criteria:\n\n* A prior diagnosis of diabetic retinopathy at the time of screening.\n* A history of ophthalmic surgery within 6 months prior to the screening date.\n* A diagnosis of type 1 diabetes mellitus.\n* Pregnancy at the time of screening.\n* Any condition that, in the opinion of the investigator, would make participation in the study infeasible or inappropriate.","ALL","19 Years",{"count":51,"type":52},340,"ESTIMATED","INTERVENTIONAL",[55],"NA","The objective of this study is to investigate the efficacy of implementing the AI-SaMD(VUNO Med®-Fundus AI™) alongside routine clinical practice for the detection of diabetic retinopathy.",[58,59,60],"Diabetic Retinopathy (DR)","Diabete Mellitus","Fundus Photography","2026-04-08",{"date":63,"type":64},"2026-04-13","ACTUAL",{"date":66,"type":64},"2026-04-07",{"date":68,"type":52},"2027-08-31",{"name":5,"class":6},1]