A Deep Learning Algorithm for Classifying Diabetic Retinopathy Using Optical Coherence Tomography Angiography

A Deep Learning Algorithm for Classifying Diabetic Retinopathy Using Optical Coherence Tomography Angiography

Purpose: To develop an automated diabetic retinopathy (DR) staging system using optical coherence tomography angiography (OCTA) images with a convolutional neural network (CNN) and to verify the feasibility of the system. Methods: In this retrospective cross-sectional study, a total of 918 data sets...

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Bibliographic Details
Main Authors: Kim, I. (Author), Lee, K. (Author), Park, D. (Author), Park, S.H (Author), Ryu, G. (Author), Sagong, M. (Author)
Format: Article
Language:English
Published: NLM (Medline) 2022
Subjects:
algorithm
Algorithms
Cross-Sectional Studies
cross-sectional study
Deep Learning
diabetes mellitus
Diabetes Mellitus
diabetic retinopathy
Diabetic Retinopathy
diagnostic imaging
Fluorescein Angiography
fluorescence angiography
human
Humans
optical coherence tomography
procedures
retina blood vessel
Retinal Vessels
Retrospective Studies
retrospective study
Tomography, Optical Coherence
Online Access:View Fulltext in Publisher

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