A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography.

ABSTRACT: As the prevalence of diabetes increases, millions of people need to be screened for diabetic retinopathy (DR). Remarkable advances in technology have made it possible to use artificial intelligence to screen DR from retinal images with high accuracy and reliability, resulting in reducing human labor by processing large amounts of data in a shorter time. We developed a fully automated classification algorithm to diagnose DR and identify referable status using optical coherence tomography angiography (OCTA) images with convolutional neural network (CNN) model and verified its feasibility by comparing its performance with that of conventional machine learning model. Ground truths for classifications were made based on ultra-widefield fluorescein angiography to increase the accuracy of data an

SUBMITTER: Ryu G 

PROVIDER: S-EPMC8626435 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature