Implementing Artificial Intelligence to Prevent Vision Loss From Diabetes
3 other identifiers
interventional
1,700
1 country
1
Brief Summary
This pragmatic clinical trial is being conducted to test the effectiveness of AI in improving screening and follow-up eye care compared to usual-care among patients with diabetes across 4 primary care clinics. This is an autonomous AI-based screening to detect diabetic eye disease at primary care visits.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for not_applicable
Started Feb 2026
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
Click on a node to explore related trials.
Study Timeline
Key milestones and dates
Study Start
First participant enrolled
February 15, 2026
CompletedFirst Submitted
Initial submission to the registry
April 23, 2026
CompletedFirst Posted
Study publicly available on registry
April 30, 2026
CompletedPrimary Completion
Last participant's last visit for primary outcome
February 1, 2027
ExpectedStudy Completion
Last participant's last visit for all outcomes
February 1, 2027
April 30, 2026
April 1, 2026
12 months
April 23, 2026
April 23, 2026
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Proportion of patients who get eye screening in the AI and usual-care arms within 5 months of the recommendation
up to 5 months
Secondary Outcomes (2)
Proportion of patients, who completed follow-up with recommended eye care in the AI and usual-care arms within 5 months of the recommendation
up to 5 months
Proportion of patients in different demographic groups who receive eye screening in the AI and usual-care arms within 5 months of the recommendation
up to 5 months
Study Arms (2)
Usual Care
ACTIVE COMPARATORPatients with diabetes will follow the clinic's usual practice, in which the primary care provider recommends an annual screening eye exam for patients with diabetes. This requires the patient to make a separate visit to see an eye care provider. Clinic staff will provide scheduling assistance per the standard scheduling procedure for the clinic.
AI Intervention
EXPERIMENTALAI intervention includes (1) acquisition of eye photos and (2) autonomous (i.e. without human oversight) AI-based identification of referrable or non-referrable eye disease at the primary care clinic.
Interventions
Eligibility Criteria
You may qualify if:
- Serve at least 267 patients with diabetes during the study period
- No point-of-care screening system in use for diabetic eye disease
- Agree to share limited identifiers data as requested
- Age 22 years or older
- Diagnosis of type 1 or 2 diabetes
- No known diabetic eye disease
- No diabetic eye exam in the past 12 months
You may not qualify if:
- Have a documented eye exam in the electronic health record within 12 months of the date of the primary care visit.
- Contraindication includes diagnosed with macular edema, severe non-proliferative retinopathy, proliferative retinopathy, radiation retinopathy, or retinal vein occlusion.
- Pregnant
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
University of Wisconsin
Madison, Wisconsin, 53792, United States
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Roomsa Channa, MD
University of Wisconsin, Madison
Study Design
- Study Type
- interventional
- Phase
- not applicable
- Allocation
- RANDOMIZED
- Masking
- NONE
- Purpose
- HEALTH SERVICES RESEARCH
- Intervention Model
- PARALLEL
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
April 23, 2026
First Posted
April 30, 2026
Study Start
February 15, 2026
Primary Completion (Estimated)
February 1, 2027
Study Completion (Estimated)
February 1, 2027
Last Updated
April 30, 2026
Record last verified: 2026-04
Data Sharing
- IPD Sharing
- Will share
- Shared Documents
- STUDY PROTOCOL, SAP, ICF