[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100474852":3},{"organization":4,"armGroups":7,"interventions":13,"overallOfficials":21,"centralContacts":25,"locations":34,"responsibleParty":53,"collaborators":55,"id":61,"slug":62,"hasResults":63,"nctId":64,"briefTitle":65,"officialTitle":66,"acronym":67,"eligibilityCriteria":68,"healthyVolunteers":63,"sex":69,"minAge":70,"maxAge":71,"enrollmentInfo":72,"targetDuration":29,"studyType":75,"phases":76,"briefSummary":78,"conditions":79,"keywords":83,"overallStatus":37,"whyStopped":29,"lastUpdateSubmitDate":85,"lastUpdatePostDateStruct":86,"startDateStruct":89,"completionDateStruct":91,"leadSponsor":93,"locationsCount":94},{"fullName":5,"class":6},"Johns Hopkins University","OTHER",[8],{"label":9,"type":6,"description":10,"interventionNames":11},"Diabetic Retinopathy Exam at the point of care","Participants will undergo a point of care diabetic retinopathy eye exam using autonomous AI. Those that test positive will be referred to Eye Care Provider for dilated eye exam.",[12],"Diagnostic Test: Point of Care Autonomous AI diabetic retinopathy exam",[14],{"type":15,"name":16,"description":17,"armGroupLabels":18,"otherNames":19},"DIAGNOSTIC_TEST","Point of Care Autonomous AI diabetic retinopathy exam","Participants will undergo point-of-care diabetic retinopathy screening using autonomous artificial intelligence software to interpret retinal images taken with a non-mydriatic fundus camera and providing an immediate result.",[9],[20],"IDx-DR",[22],{"name":23,"affiliation":5,"role":24},"Risa M Wolf, MD","PRINCIPAL_INVESTIGATOR",[26,31],{"name":23,"role":27,"phone":28,"phoneExt":29,"email":30},"CONTACT","4109556463",null,"RWolf@jhu.edu",{"name":32,"role":27,"phone":29,"phoneExt":29,"email":33},"Alvin Liu, MD","tliu25@jhmi.edu",[35],{"facility":36,"status":37,"city":38,"state":39,"zip":40,"country":41,"countryCode":42,"cosmosGeoPoint":43,"geoPoint":48,"contacts":49},"Johns Hopkins Pediatric Diabetes Center","RECRUITING","Baltimore","Maryland","21287","United States","US",{"type":44,"coordinates":45},"Point",[46,47],-76.61219,39.29038,{"lat":47,"lon":46},[50],{"name":23,"role":27,"phone":51,"phoneExt":29,"email":52},"410-955-6463","rwolf@jhu.edu",{"type":54,"investigatorFullName":29,"investigatorTitle":29,"investigatorAffiliation":29,"oldNameTitle":29,"oldOrganization":29},"SPONSOR",[56,59],{"name":57,"class":58},"National Eye Institute (NEI)","NIH",{"name":60,"class":6},"Juvenile Diabetes Research Foundation","100474852","access-2-ai-for-pediatric-diabetic-eye-exams-study-2-100474852",false,"NCT05463289","ACCESS 2: AI for pediatriC diabetiC Eye examS Study 2","Implementing Digital Retinal Exams Into Comprehensive Pediatric Diabetes Care","ACCESS2","Inclusion Criteria:\n\nMeets American Diabetes Association (ADA) criteria for diabetic retinopathy screening:\n\n* Diagnosis of Type 1 diabetes for ≥3 years, and age 11 or in puberty\n* Diagnosis of Type 2 diabetes\n\nEnriched cohort:\n\n* Patients with Type 1 or Type 2 diabetes,\n* 8-21 years of age with known diabetic retinopathy (true positives).\n* No time limit on last diabetic eye exam.\n\nExclusion Criteria:\n\n* Known diabetic eye exam in the last 12 months","ALL","8 Years","21 Years",{"count":73,"type":74},500,"ESTIMATED","INTERVENTIONAL",[77],"NA","The purpose of this study is to determine if use of a nonmydriatic fundus camera using autonomous artificial intelligence software at the point of care increases the proportion of underserved youth with diabetes screened for diabetic retinopathy, and to determine the diagnostic accuracy of the autonomous AI system in detecting diabetic retinopathy from retinal images of youth with diabetes.",[80,81,82],"Type 1 Diabetes","Type 2 Diabetes","Cystic Fibrosis-related Diabetes",[84],"Diabetic Retinopathy","2026-06-30",{"date":87,"type":88},"2026-07-02","ACTUAL",{"date":90,"type":88},"2022-07-11",{"date":92,"type":74},"2026-09-30",{"name":5,"class":6},1]