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.
- Study Type: Observational
- Study Design
- Time Perspective: Prospective
- Study Primary Completion Date: November 2021
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.
- Other: In-Person Eye Examination
- Dilated in-person eye examination by a board-certified ophthalmologist or retinal fellow.
Arms, Groups and Cohorts
- Diabetic patients w. risk of retinopathy
- 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.
Clinical Trial Outcome Measures
- Proportion of patients accurately diagnosed with retinopathy
- Time Frame: 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.
Participating in This Clinical Trial
- 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).
Gender Eligibility: All
Minimum Age: 18 Years
Maximum Age: N/A
Are Healthy Volunteers Accepted: No
- Lead Sponsor
- Charles Drew University of Medicine and Science
- National Library of Medicine (NLM)
- Provider of Information About this Clinical Study
- Overall Official(s)
- Omolola Ogunyemi, PhD, Principal Investigator, Charles Drew University of Medicine and Science
- Overall Contact(s)
- Junko Nishitani, PhD, 323-563-4966, firstname.lastname@example.org
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