AIMC Topic: Glaucoma

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A Review of Deep Learning for Screening, Diagnosis, and Detection of Glaucoma Progression.

Translational vision science & technology
UNLABELLED: Because of recent advances in computing technology and the availability of large datasets, deep learning has risen to the forefront of artificial intelligence, with performances that often equal, or sometimes even exceed, those of human s...

Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma.

The British journal of ophthalmology
BACKGROUND/AIM: To train and validate the prediction performance of the deep learning (DL) model to predict visual field (VF) in central 10° from spectral domain optical coherence tomography (SD-OCT).

Effect of color information on the diagnostic performance of glaucoma in deep learning using few fundus images.

International ophthalmology
PURPOSE: The purpose of this study was to evaluate the accuracy of the convolutional neural network (CNN) model in glaucoma identification with three primary colors (red, green, blue; RGB) and split color channels using fundus photographs with a smal...

Explaining the Rationale of Deep Learning Glaucoma Decisions with Adversarial Examples.

Ophthalmology
PURPOSE: To illustrate what is inside the so-called black box of deep learning models (DLMs) so that clinicians can have greater confidence in the conclusions of artificial intelligence by evaluating adversarial explanation on its ability to explain ...

WGAN domain adaptation for the joint optic disc-and-cup segmentation in fundus images.

International journal of computer assisted radiology and surgery
PURPOSE: The cup-to-disc ratio (CDR), a clinical metric of the relative size of the optic cup to the optic disc, is a key indicator of glaucoma, a chronic eye disease leading to loss of vision. CDR can be measured from fundus images through the segme...

AxoNet: A deep learning-based tool to count retinal ganglion cell axons.

Scientific reports
In this work, we develop a robust, extensible tool to automatically and accurately count retinal ganglion cell axons in optic nerve (ON) tissue images from various animal models of glaucoma. We adapted deep learning to regress pixelwise axon count de...

A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells).

Expert review of molecular diagnostics
BACKGROUND: A key objective in glaucoma is to identify those at risk of rapid progression and blindness. Recently, a novel first-in-man method for visualising apoptotic retinal cells called DARC (Detection-of-Apoptosing-Retinal-Cells) was reported. T...

Predicting Glaucoma before Onset Using Deep Learning.

Ophthalmology. Glaucoma
PURPOSE: To assess the accuracy of deep learning models to predict glaucoma development from fundus photographs several years before disease onset.

Effects of Study Population, Labeling and Training on Glaucoma Detection Using Deep Learning Algorithms.

Translational vision science & technology
PURPOSE: To compare performance of independently developed deep learning algorithms for detecting glaucoma from fundus photographs and to evaluate strategies for incorporating new data into models.