Type 1 DiabetesACCESS2NCT05463289
ACCESS 2: AI for pediatriC diabetiC Eye examS Study 2
A study testing Point of Care Autonomous AI diabetic retinopathy exam in people with Type 1 Diabetes and Type 2 Diabetes, run by Johns Hopkins University at 1 site in United States.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 8 to 21 years
- Conditions the study is for
- Type 1 Diabetes, Type 2 Diabetes, Cystic Fibrosis-related Diabetes
- What is being tested?
- Point of Care Autonomous AI diabetic retinopathy exam
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 500 participants
- Where
- 1 siteUnited States
- Study dates
- 11 Jul 2022to 30 Dec 2026
- Phase
- Interventional
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 8 to 21 years
- All sexes
- No healthy volunteers
- Child, Adult
Who may be able to join
- 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
- Enriched cohort:
- Patients with Type 1 or Type 2 diabetes,
- 8-21 years of age with known diabetic retinopathy (true positives).
- No time limit on last diabetic eye exam.
Who cannot join
- Known diabetic eye exam in the last 12 months
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Alvin Liu, MD contact
United States1 site, 1 recruiting
- Recruiting
Johns Hopkins Pediatric Diabetes Center
Baltimore, Maryland, 21287
Risa M Wolf, MD, 410-955-6463, rwolf@jhu.edu
About this study
Official title: Implementing Digital Retinal Exams Into Comprehensive Pediatric Diabetes Care
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.
Read the detailed description
This study will recruit up to 500 individuals ages 8-21 with type 1 or type 2 diabetes. In this study, 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.
In the AI for ChildrenS Diabetic Eye ExamS Study (ACCESS2), 398 participants will be enrolled to determine if point of care autonomous AI increases the proportion of minority and underserved youth screened for diabetic retinopathy. The autonomous AI interpretation will also be compared to consensus grading of retinal specialists to determine if there is agreement and to determine the diagnostic accuracy of the system in youth.
A cohort of youth with known diabetic retinopathy (true positives) will also be enrolled as an enriched population to determine the diagnostic accuracy of autonomous AI compared to the prognostic standard interpretation of a central reading center.
- Being tested
- Point of Care Autonomous AI diabetic retinopathy exam
- Keywords
- Diabetic Retinopathy
- Medical subject headings
- Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Diabetic Retinopathy
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Point of Care Autonomous AI diabetic retinopathy examDiagnostic test
Also called IDx-DR
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.
| Study group | Role | What they receive |
|---|---|---|
| Diabetic Retinopathy Exam at the point of care | Other | 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. Receives: Diagnostic Test: Point of Care Autonomous AI diabetic retinopathy exam |
What is being measured
The primary outcome is the main question the study is designed to answer.
Proportion screened for diabetic retinopathy
Equivalence in proportion screened for diabetic retinopathy of white and non-white youth with autonomous AI
Measured: Day 1
Secondary outcomes (4)
Percentage of agreement in interpretation of retinal images
Agreement in interpretation of retinal images between autonomous AI and consensus grading by ophthalmologists
Measured: Day 1
Sensitivity of autonomous AI vs. prognostic standard
Sensitivity of autonomous AI in detecting diabetic retinopathy in youth compared to the prognostic standard. This will be analyzed in the ACCESS2 trial cohort alone, and also in the ACCESS2 trial cohort with the enriched cohort of youth with known diabetic retinopathy.
Measured: Day 1
Specificity of autonomous AI vs. prognostic standard
Specificity of autonomous AI in detecting diabetic retinopathy in youth compared to the prognostic standard. This will be analyzed in the ACCESS2 trial cohort alone, and also in the ACCESS2 trial cohort with the enriched cohort of youth with known diabetic retinopathy.
Measured: Day 1
Proportion with diabetic retinopathy
Proportion of participants with diabetic retinopathy, including none, mild, moderate or severe DR.
Measured: Day 1
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Jul 2022
- Study start11 Jul 2022
- Primary completion30 Dec 2026 (estimated)
- Study completion30 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)Papers the sponsor has linked to this study.
- Other trials for Type 1 DiabetesStudies of the same condition, open and under way.
- More trials run by Johns Hopkins UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Johns Hopkins University
- Sponsor type
- University, hospital or other
- Collaborators
- National Eye Institute (NEI), Juvenile Diabetes Research Foundation
- Responsible party
- Sponsor
- Investigators
- Risa M Wolf, MD, Principal investigator, Johns Hopkins University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05463289
- Sponsor's protocol ID
- IRB00180692
- Other IDs
- 1R01EY033233-01
- Acronym
- ACCESS2
- Status last verified
- Sep 2026
- First submitted
- 14 Jul 2022
- Last updated
- 23 Sep 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- No
Publications
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.
PubMed 32126819Thomas 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.
PubMed 34042939Wolf 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.
PubMed 32880616Wolf 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.
PubMed 33479160Porter 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.
PubMed 32410329