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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT05463289
Other study ID # IRB00180692
Secondary ID 1R01EY033233-01
Status Recruiting
Phase N/A
First received
Last updated
Start date July 11, 2022
Est. completion date October 1, 2024

Study information

Verified date June 2024
Source Johns Hopkins University
Contact Risa M Wolf, MD
Phone 4109556463
Email RWolf@jhu.edu
Is FDA regulated No
Health authority
Study type Interventional

Clinical 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.


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 information / eligibility

Status Recruiting
Enrollment 500
Est. completion date October 1, 2024
Est. primary completion date September 1, 2024
Accepts healthy volunteers No
Gender All
Age group 8 Years to 21 Years
Eligibility 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


Intervention

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.

Locations

Country Name City State
United States Johns Hopkins Pediatric Diabetes Center Baltimore Maryland

Sponsors (3)

Lead Sponsor Collaborator
Johns Hopkins University Juvenile Diabetes Research Foundation, National Eye Institute (NEI)

Country where clinical trial is conducted

United States, 

References & Publications (5)

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

Outcome

Type Measure Description Time frame Safety issue
Primary Proportion screened for diabetic retinopathy Equivalence in proportion screened for diabetic retinopathy of white and non-white youth with autonomous AI 2 years
Secondary Percentage of agreement in interpretation of retinal images Agreement in interpretation of retinal images between autonomous AI and consensus grading by ophthalmologists 2 years
Secondary Diagnostic Accuracy Sensitivity, specificity and diagnosability of autonomous AI in detecting diabetic retinopathy in youth compared to the prognostic standard Day 1
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