Predicting Diabetic Retinopathy From Risk Factor Data and Digital Retinal Images

Diabetic Retinopathy Clinical Trial

Official title:

Predicting Diabetic Retinopathy From Risk Factor Data and Digital Retinal Images

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT03694145
• Other study ID #: 1218589-3
• Secondary ID: R01LM012309
• Status: Active, not recruiting
• Start date: October 25, 2018
• Est. completion date: July 31, 2024

Study information

• Verified date: October 2023
• Source: Charles Drew University of Medicine and Science
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 300
• Est. completion date: July 31, 2024
• Est. primary completion date: July 31, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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).

Related Conditions & MeSH terms

• Diabetic Macular Edema
• Diabetic Retinopathy
• Macular Edema
• Retinal Diseases

Intervention

Other:
• In-Person Eye Examination
Dilated in-person eye examination by a board-certified ophthalmologist or retinal fellow.

Locations

Country	Name	City	State
United States	Charles R. Drew University of Medicine and Science	Los Angeles	California
United States	Los Angeles Department of Public Health	Los Angeles	California
United States	University of California - Los Angeles	Los Angeles	California

Sponsors (4)

Lead Sponsor	Collaborator
Charles Drew University of Medicine and Science	Los Angeles County Department of Public Health, National Library of Medicine (NLM), University of California, Los Angeles

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Proportion of patients accurately diagnosed with retinopathy	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.	11/2022