Glaucoma

An Artificial Intelligence Approach to Assess Spatial Patterns of Retinal Nerve Fiber Layer Thickness Maps in Glaucoma.

PURPOSE: The purpose of this study was to classify the spatial patterns of retinal nerve fiber layer...

The role of neuroinflammation in the pathogenesis of glaucoma neurodegeneration.

The chapter is a review enclosed in the volume "Glaucoma: A pancitopatia of the retina and beyond." ...

Artificial intelligence and deep learning in glaucoma: Current state and future prospects.

Over the past few years, there has been an unprecedented and tremendous excitement for artificial in...

Artificial Intelligence and Glaucoma: Illuminating the Black Box.

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

UNLABELLED: Because of recent advances in computing technology and the availability of large dataset...

An Intelligent Opportunistic Routing Algorithm for Wireless Sensor Networks and Its Application Towards e-Healthcare.

The lifetime of a node in wireless sensor networks (WSN) is directly responsible for the longevity o...

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

BACKGROUND/AIM: To train and validate the prediction performance of the deep learning (DL) model to ...

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

PURPOSE: The purpose of this study was to evaluate the accuracy of the convolutional neural network ...

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

PURPOSE: To illustrate what is inside the so-called black box of deep learning models (DLMs) so that...

Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa.

Ophthalmological analysis plays a vital role in the diagnosis of various eye diseases, such as glauc...

Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology.

With the advancement of computational power, refinement of learning algorithms and architectures, an...

Applications of Artificial Intelligence in the Screening of Glaucoma in China.

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

PURPOSE: The cup-to-disc ratio (CDR), a clinical metric of the relative size of the optic cup to the...

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

In this work, we develop a robust, extensible tool to automatically and accurately count retinal gan...

The usefulness of the Deep Learning method of variational autoencoder to reduce measurement noise in glaucomatous visual fields.

The aim of the study was to investigate the usefulness of processing visual field (VF) using a varia...

Predicting the Glaucomatous Central 10-Degree Visual Field From Optical Coherence Tomography Using Deep Learning and Tensor Regression.

PURPOSE: To predict the visual field (VF) of glaucoma patients within the central 10° from optical c...

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

BACKGROUND: A key objective in glaucoma is to identify those at risk of rapid progression and blindn...

Predicting Glaucoma before Onset Using Deep Learning.

PURPOSE: To assess the accuracy of deep learning models to predict glaucoma development from fundus ...

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

PURPOSE: To compare performance of independently developed deep learning algorithms for detecting gl...

Machine learning applied to retinal image processing for glaucoma detection: review and perspective.

INTRODUCTION: This is a systematic review on the main algorithms using machine learning (ML) in reti...

Artificial Intelligence Mapping of Structure to Function in Glaucoma.

PURPOSE: To develop an artificial intelligence (AI)-based structure-function (SF) map relating retin...