In-Person Eye Examination
OtherDilated in-person eye examination by a board-certified ophthalmologist or retinal fellow.
NCT Number: NCT03694145
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.
This study is active but is not currently recruiting participants.
18 year and older
All sexes
Observational
Charles R. Drew University of Medicine and Science, Los Angeles, California, United States
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.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Dilated in-person eye examination by a board-certified ophthalmologist or retinal fellow.
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.
Charles Drew University of Medicine and Science
Other
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