Purpose To compare the diagnostic accuracy and explainability of a Vision Transformer
deep learning technique, Data-efficient image Transformer (DeiT), and ResNet-50, trained …

The ability of deep learning architectures to identify glaucomatous optic neuropathy (GON)
in fundus photographs was evaluated. A large database of fundus photographs (n= 14,822) …

Purpose To validate a deep residual learning algorithm to diagnose glaucoma from fundus
photography using different fundus cameras at different institutes. Design Cross-sectional …

Primary open-angle glaucoma (POAG) is a leading cause of irreversible blindness
worldwide. Although deep learning methods have been proposed to diagnose POAG, it …

A plethora of classification models for the detection of glaucoma from fundus images have
been proposed in recent years. Often trained with data from a single glaucoma clinic, they …

Purpose To assess the accuracy of deep learning models to predict glaucoma development
from fundus photographs several years before disease onset. Design Algorithm …

Purpose To develop and validate a deep learning (DL) algorithm that predicts referable
glaucomatous optic neuropathy (GON) and optic nerve head (ONH) features from color …

Purpose To develop a deep learning approach based on deep residual neural network
(ResNet101) for the automated detection of glaucomatous optic neuropathy (GON) using …

The Purpose of the study was to develop a deep residual learning algorithm to screen for
glaucoma from fundus photography and measure its diagnostic performance compared to …

Background Deep learning has been widely used for glaucoma diagnosis. However, there is
no clinically validated algorithm for glaucoma incidence and progression prediction. This …