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Diabetic Retinopathy Screening Point-of-Care Artificial Intelligence

NCT06721351 · Status: ACTIVE_NOT_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 548

Last updated 2025-11-06

No results posted yet for this study

Summary

This research study is being conducted to improve eye care by using artificial intelligence (AI) to make diabetic eye screenings faster and more accessible. AI technology mimics human decision-making, enabling computers and systems to analyze medication information. Specifically for this screening, AI examines digital images of the eye and based on that information, may identify if a participant has diabetic retinopathy. It can assist doctors in making decisions about a participant's diagnosis, treatment or care plans to improve patient care. This is a collaboration between San Ysidro Health (SYHealth), University of California, San Diego (UC San Diego), and Eyenuk. The Kaiser Permanente Augmented Intelligence in Medicine and Healthcare Initiative (AIM-HI) awarded SYHealth funds to demonstrate the value of AI technologies in diverse, real-world settings.

Conditions

  • Diabetic Retinopathy (DR)

Interventions

DIAGNOSTIC_TEST

Diabetic Retinopathy screening Point of Care Artificial Intelligence

Random assignment of participants to intervention and control groups minimizes confounding variables. This ensures that any observed differences in outcomes are likely due to the intervention itself and not pre-existing differences between the groups. Participants of the DRES-POCAI study will be randomized into two groups: usual care (receive a retinal screening by an optometrist) or intervention group (receive a retinal screening using a special camera and EyeArt® system, an autonomous AI-based DR screening). The randomization will occur after consenting and completing the surveys, prior to conducting the DR screening process (for those randomized to the intervention group).

Sponsors & Collaborators

Principal Investigators

  • Fatima Muñoz, MD, MPH · San Ysidro Health

  • Nicole Stadnick, PhD · University of California, San Diego

Study Design

Allocation
RANDOMIZED
Purpose
SCREENING
Masking
NONE
Model
PARALLEL

Eligibility

Min Age
22 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2024-07-27
Primary Completion
2025-12-31
Completion
2026-04-30
FDA Device
Yes

Countries

  • United States

Study Locations

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Entities

Companies

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06721351 on ClinicalTrials.gov