The purpose of this study is to create a patient-centric environment for early detection of DR with AI-driven solutions.
The purpose of this study is to create a patient-centric environment for early detection of DR with AI-driven solutions. This study is planned as a follow-up. Participants who meet the eligibility criteria will be recruited from sites staffed by the trained photographers. After assessing eligibility and securing written informed consent, fundus photographs will be captured using a nonmydriatic ocular fundus camera. Images will be taken according to a specific RAssbyAI Check Eye's imaging protocol provided to camera operator, and then analyzed by the RAssbyAI Check Eye's. The photography protocol consists of two images of the ocular fundus (one optic disc centered, one fovea centered).
Study Type
OBSERVATIONAL
Enrollment
660
using artificial intelligence to identify diabetic retinopathy in the early stages using fundus photography.
The Filatov Institute of Eye Diseases and Tissue Therapy
Odesa, Ukraine
RECRUITINGThe accuracy
The accuracy of detecting of DR
Time frame: Baseline-Month 12
The percent of invalid images
The percent of invalid images for analysing by neural network
Time frame: Baseline-Month 12
The percent of false positive detection of DR
The percent of false positive detection of DR in individuals without DR
Time frame: Baseline-Month 12
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