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ACCESS 2: AI for pediatriC diabetiC Eye examS Study 2

Not Applicable
Recruiting
Conditions
Type 1 Diabetes
Type 2 Diabetes
Cystic Fibrosis-related Diabetes
Interventions
Diagnostic Test: Point of Care Autonomous AI diabetic retinopathy exam
Registration Number
NCT05463289
Lead Sponsor
Johns Hopkins University
Brief 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.

Detailed Description

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.

Recruitment & Eligibility

Status
RECRUITING
Sex
All
Target Recruitment
500
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
Exclusion Criteria
  • Known diabetic eye exam in the last 12 months

Study & Design

Study Type
INTERVENTIONAL
Study Design
SINGLE_GROUP
Arm && Interventions
GroupInterventionDescription
Diabetic Retinopathy Exam at the point of carePoint of Care Autonomous AI diabetic retinopathy examParticipants 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.
Primary Outcome Measures
NameTimeMethod
Proportion screened for diabetic retinopathy2 years

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

Secondary Outcome Measures
NameTimeMethod
Diagnostic AccuracyDay 1

Sensitivity, specificity and diagnosability of autonomous AI in detecting diabetic retinopathy in youth compared to the prognostic standard

Percentage of agreement in interpretation of retinal images2 years

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

Trial Locations

Locations (1)

Johns Hopkins Pediatric Diabetes Center

🇺🇸

Baltimore, Maryland, United States

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