Leveraging Artificial Intelligence to Prevent Vision Loss From Diabetes

NCT06763952 | Not Yet Recruiting

• 5 other identifiers: 2026-0446Protocol Version 2/20/26A5360002024-00301R01EY035994
• Study Type: interventional
• Target: 4,000
• Locations: 1 country
• Sites: 1

Brief Summary

This study aims to investigate whether a novel artificial intelligence based screening strategy (AI-Based point of caRe, Incorporating Diagnosis, SchedulinG, and Education or AI-BRIDGE), which allows primary care providers to screen patients for vision-threatening diabetic eye disease in the primary care clinic, improves screening and follow-up care rates across race/ethnicity groups and reduces racial/ethnic disparities in screening.

Conditions

Diabetes Mellitus; Diabetic Retinopathy

Keywords

Artificial Intelligence; AI; diabetic eye disease; cluster randomized trial; pragmatic trial

Outcome Measures

Primary Outcomes (1)

• Proportion of Participants Who Follow Up With Recommended Eye Care

  Proportion of patients, by race and ethnicity, who follow-up with recommended eye care in the AI-BRIDGE and usual-care arms within 6 months of the recommendation.

  up to 6 months

Secondary Outcomes (2)

• Difference in Proportion of White vs Hispanic and White vs Black Participants Who Get Eye Screening

  up to 6 months

• Proportion of Participants By Race and Ethnicity Who Get Eye Screening

  up to 6 months

Other Outcomes (1)

• Level of Diabetic Retinopathy

  up to 6 months

Study Arms (2)

Usual Care Screening

NO INTERVENTION

Primary care providers refer patients with diabetes to an eye care provider for a dilated eye exam. Patients are provided with culturally adapted diabetic eye disease educational materials similar to that provided to patients in the AI-BRIDGE group.

AI-BRIDGE

EXPERIMENTAL

AI-based eye screening program called AI-Based point of caRe, Incorporating Diagnosis, SchedulinG, and Education (AI-BRIDGE). Eye photos of the patients will be obtained in the primary care clinic during a patient's regular primary care visit by a trained technician. Images will be reviewed using autonomous artificial-intelligence (AI) algorithm (Digital Diagnostics). Patients with referrable diabetic retinopathy are detected, and assisted with scheduling an in-person follow-up eye care visits. All patients irrespective of diabetic retinopathy status are also provided culturally adapted educational material on diabetic eye disease.

Other: AI

Interventions

AI OTHER

AI-based eye screening program

AI-BRIDGE

Eligibility Criteria

• Age: 22 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Diagnosed with type 1 or 2 diabetes
• No known diabetic eye disease
• Medicaid as their primary insurance
• Not had an eye exam in the prior year

Sponsors & Collaborators

• University of Wisconsin, Madison: lead
• National Eye Institute (NEI): collaborator

Study Sites (1)

UW School of Medicine and Public Health

Madison, Wisconsin, 53792, United States

Location

MeSH Terms

Conditions

Diabetic Retinopathy; Diabetes Mellitus

Condition Hierarchy (Ancestors)

Retinal Diseases; Eye Diseases; Diabetic Angiopathies; Vascular Diseases; Cardiovascular Diseases; Diabetes Complications; Endocrine System Diseases; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases

Study Officials

• Roomasa Channa

  UW School of Medicine and Public Health

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Mozhdeh Bahrainian

CONTACT

608-262-9955; bahrainian@wisc.edu

Roomasa Channa

CONTACT

rchanna@wisc.edu

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: SEQUENTIAL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: A stepped-wedge cluster randomized clinical trial will be conducted to evaluate the effectiveness of two standard diabetic retinopathy screenings across 9 underserved primary care clinics.

Study Record Dates

• First Submitted: December 26, 2024
• First Posted: January 8, 2025
• Study Start (Estimated): August 1, 2026
• Primary Completion (Estimated): May 1, 2030
• Study Completion (Estimated): May 1, 2030
• Last Updated: April 29, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will share

Locations

US (1)