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NCT Number: NCT05463289

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, and to determine the diagnostic accuracy of the autonomous AI system in detecting diabetic retinopathy from retinal images of youth with diabetes.

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Key information

Conditions

Type 1 Diabetes Autoimmune Diseases Cardiovascular Diseases Cystic Fibrosis-related Diabetes Diabetes Complications Diabetes Mellitus Diabetes Mellitus, Type 1 Diabetes Mellitus, Type 2 Diabetic Angiopathies Diabetic Retinopathy Endocrine System Diseases Eye Diseases Glucose Metabolism Disorders Immune System Diseases Metabolic Diseases Nutritional and Metabolic Diseases Retinal Diseases Type 2 Diabetes Vascular Diseases

Age range

8 year–21 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Johns Hopkins Pediatric Diabetes Center

Baltimore, Maryland, 21287, United States

Location status: Recruiting

Location contact

Risa M Wolf, MD

CONTACT

[email protected]

410-955-6463

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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.

Exclusion criteria

  • Known diabetic eye exam in the last 12 months

Treatment and study plan

Point of Care Autonomous AI diabetic retinopathy exam

Diagnostic Test

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.

Other names: IDx-DR

Primary outcomes

  1. Proportion screened for diabetic retinopathy

    Time frame: Day 1

    Equivalence in proportion screened for diabetic retinopathy of white and non-white youth with autonomous AI

Secondary outcomes

  1. Percentage of agreement in interpretation of retinal images

    Time frame: Day 1

    Agreement in interpretation of retinal images between autonomous AI and consensus grading by ophthalmologists

  2. Sensitivity of autonomous AI vs. prognostic standard

    Time frame: Day 1

    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.

  3. Specificity of autonomous AI vs. prognostic standard

    Time frame: Day 1

    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.

  4. Proportion with diabetic retinopathy

    Time frame: Day 1

    Proportion of participants with diabetic retinopathy, including none, mild, moderate or severe DR.

Study contacts

Contact information is provided by the study sponsor or research team.

Alvin Liu, MD

CONTACT

[email protected]

Risa M Wolf, MD

CONTACT

[email protected]

4109556463

Sponsors and collaborators

Lead sponsor

Johns Hopkins University

Other

Collaborators

  • Juvenile Diabetes Research Foundation
  • National Eye Institute (NEI)

Registry information

Official study title

Implementing Digital Retinal Exams Into Comprehensive Pediatric Diabetes Care

Acronym: ACCESS2

Important dates

Study start
2022
Primary completion
2026
Study completion
2026
First posted
Jul 18, 2022
Registry last updated
Jul 2, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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