Predicting Diabetic Retinopathy From Risk Factor Data and Digital Retinal Images

Active, not recruiting

• Conditions: Diabetic Retinopathy; Diabetic Macular Edema
• Interventions: Other: In-Person Eye Examination

• Registration Number: NCT03694145
• Lead Sponsor: Charles Drew University of Medicine and Science

Brief Summary

The objective of this study is to compare the results of a deep learning approach to diabetic retinopathy assessment with results from (1) an in-person examination with an ophthalmologist, and (2) the assessments of optometrists involved in a teleretinal screening program.

Detailed Description

This study represents the third aim of a grant with five aims. The study will compare and evaluate the predictive accuracy of: (a) machine learning models developed to grade diabetic retinopathy and assess the presence or absence of diabetic macular edema and (b) the assessments of optometrist readers, both from digital retinal images, against standard of care dilated retinal examinations by board-certified ophthalmologists and/or retinal-specialty fellows for 300 diabetic patients utilizing a Los Angeles County reading center.

For the study, the investigators will recruit 300-500 eligible diabetic patients for in-person eye examinations performed by board certified ophthalmologists and/or retinal-specialty fellows at Los Angeles County reading centers. The study will take place over the course of two visits: a teleretinal screening and an in-person eye examination.

The in-person dilated eye examinations that the study participants will participate in and be compensated for follow the usual standard of care that patients receive in a setting that does not utilize teleretinal screening. Yearly dilated eye examinations are standard of care for all persons with diabetes.

Study Timeline

• Study First Submitted: 2018-10-01
• Study First Submitted QC: 2018-10-01
• Study First Posted: 2018-10-03
• Study Start: 2018-10-25
• Status Verified: 2024-11
• Last Update Submitted: 2025-02-12
• Last Update Posted: 2025-02-14
• Primary Completion: 2025-05-31
• Study Completion: 2025-07-31

Recruitment & Eligibility

• Status: ACTIVE_NOT_RECRUITING
• Sex: All
• Target Recruitment: 300

Inclusion Criteria

• Patients diagnosed with Type I or Type II Diabetes
• Patients who are over the age of 18 years
• Patients who have not previously been seen in the current year by the LACDHS Teleretinal Diabetic Retinopathy Screening Program and Reading Center
• Patients whose teleretinal screening exam images yield readable results

Exclusion Criteria

• Patients under the age of 18 years
• Patients with gestational diabetes
• Patients who have previously been seen in the current year by LACDHS's Teleretinal Diabetic Retinopathy Screening Program and Reading Center
• Patients whose teleretinal screening exam images do not yield readable results, as gradable images are needed for later comparison against ophthalmologist reads.
• Previously eligible patients who do not return for an in-person eye exam within 3 months of receiving a teleretinal screening (In order for the results of the teleretinal screening and in-person eye examinations to yield similar information, patients who do not return for their in-person eye exam within 3 months of their teleretinal screening will not be able to remain in the study. This is because significant eye changes not documented by the teleretinal screening may occur after a 3-month period).

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Diabetic patients w. risk of retinopathy	In-Person Eye Examination	The 300-500 patients to be enrolled for the study are diabetic patients normally seen by the Los Angeles County Department of Health Services (LACDHS) Teleretinal Diabetic Retinopathy Screening Program and Reading Center. In addition to receiving their recommended LACDHS annual teleretinal screening, for the study, participants will receive an additional in-person eye examination.

Primary Outcome Measures

Name	Time	Method
Proportion of patients accurately diagnosed with retinopathy	11/2022	Proportion of patients accurately diagnosed with retinopathy using machine learning versus proportion accurately diagnosed by teleretinal screening optometrists with in-person eye examinations by ophthalmologists used as a gold standard.

Trial Locations

Locations (3)

Charles R. Drew University of Medicine and Science; 🇺🇸 Los Angeles, California, United States

University of California - Los Angeles; 🇺🇸 Los Angeles, California, United States

Los Angeles Department of Public Health; 🇺🇸 Los Angeles, California, United States