ACCESS2: ACCESS 2: AI for pediatriC diabetiC Eye examS Study 2
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.
|Condition or Disease||Intervention/Treatment||Phase|
This study will recruit 500 individuals ages 8-21 with type 1 and type 2 diabetes. Participants will undergo a point-of-care diabetic eye exam using autonomous AI software on a non-mydriatic fundus camera. Participants will receive the diabetic eye exam results immediately from the autonomous AI system, and if abnormal will be referred to an eye care provider for a dilated eye exam. The autonomous AI interpretation will also be compared to consensus grading of retinal specialists to determine if there is agreement.
Arms and Interventions
|Other: 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.
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.
Primary Outcome Measures
- Proportion screened for diabetic retinopathy [2 years]
Equivalence in proportion screened for diabetic retinopathy of white and non-white youth with autonomous AI
Secondary Outcome Measures
- Percentage of agreement in interpretation of retinal images [2 years]
Agreement in interpretation of retinal images between autonomous AI and consensus grading by ophthalmologists
Meets American Diabetes Association (ADA) criteria for diabetic retinopathy screening:
Diagnosis of Type 1 diabetes for ≥3 years, and age 11 or in puberty
Diagnosis of Type 2 diabetes
- Known diabetic eye exam in the last 12 months
Contacts and Locations
|1||Johns Hopkins Pediatric Diabetes Center||Baltimore||Maryland||United States||21287|
Sponsors and Collaborators
- Johns Hopkins University
- National Eye Institute (NEI)
- Principal Investigator: Risa M Wolf, MD, Johns Hopkins University
Study Documents (Full-Text)None provided.
- Channa R, Wolf R, Abramoff MD. Autonomous Artificial Intelligence in Diabetic Retinopathy: From Algorithm to Clinical Application. J Diabetes Sci Technol. 2021 May;15(3):695-698. doi: 10.1177/1932296820909900. Epub 2020 Mar 4.
- Porter M, Channa R, Wagner J, Prichett L, Liu TYA, Wolf RM. Prevalence of diabetic retinopathy in children and adolescents at an urban tertiary eye care center. Pediatr Diabetes. 2020 Aug;21(5):856-862. doi: 10.1111/pedi.13037. Epub 2020 May 31.
- Thomas CG, Channa R, Prichett L, Liu TYA, Abramoff MD, Wolf RM. Racial/Ethnic Disparities and Barriers to Diabetic Retinopathy Screening in Youths. JAMA Ophthalmol. 2021 Jul 1;139(7):791-795. doi: 10.1001/jamaophthalmol.2021.1551.
- Wolf RM, Channa R, Abramoff MD, Lehmann HP. Cost-effectiveness of Autonomous Point-of-Care Diabetic Retinopathy Screening for Pediatric Patients With Diabetes. JAMA Ophthalmol. 2020 Oct 1;138(10):1063-1069. doi: 10.1001/jamaophthalmol.2020.3190.
- Wolf RM, Liu TYA, Thomas C, Prichett L, Zimmer-Galler I, Smith K, Abramoff MD, Channa R. The SEE Study: Safety, Efficacy, and Equity of Implementing Autonomous Artificial Intelligence for Diagnosing Diabetic Retinopathy in Youth. Diabetes Care. 2021 Mar;44(3):781-787. doi: 10.2337/dc20-1671. Epub 2021 Jan 21.