Glaucoma

Glaucoma

Latest AI and machine learning research in glaucoma for healthcare professionals.

5,142 articles

Ophthalmology Subcategories: Glaucoma Ophthalmology Refractive Surgery

Showing 295-315 of 5,142 articles

Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color Fundus Images.

This study aimed to develop a machine learning-based algorithm for glaucoma diagnosis in patients wi...

Feb 2019 PMID: 30911364

Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation.

Glaucoma is a serious ocular disorder for which the screening and diagnosis are carried out by the e...

Feb 2019 PMID: 30762573

Can Artificial Intelligence Make Screening Faster, More Accurate, and More Accessible?

Diabetic retinopathy, glaucoma, and age-related macular degeneration are leading causes of vision lo...

Dec 2018 PMID: 30556381

Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques.

BACKGROUND AND OBJECTIVE: Glaucoma is a ocular disorder which causes irreversible damage to the reti...

Dec 2018 PMID: 30590290

A deep learning approach to automatic detection of early glaucoma from visual fields.

PURPOSE: To investigate the suitability of multi-scale spatial information in 30o visual fields (VF)...

Nov 2018 PMID: 30485270

A deep learning model for the detection of both advanced and early glaucoma using fundus photography.

PURPOSE: To build a deep learning model to diagnose glaucoma using fundus photography.

Nov 2018 PMID: 30481205

Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs.

The ability of deep learning architectures to identify glaucomatous optic neuropathy (GON) in fundus...

Nov 2018 PMID: 30420630

Artificial intelligence and deep learning in ophthalmology.

Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in r...

Oct 2018 PMID: 30361278

Using Kalman Filtering to Forecast Disease Trajectory for Patients With Normal Tension Glaucoma.

PURPOSE: To determine whether a machine learning technique called Kalman filtering (KF) can accurate...

Oct 2018 PMID: 30336130

Using Deep Learning and Transfer Learning to Accurately Diagnose Early-Onset Glaucoma From Macular Optical Coherence Tomography Images.

PURPOSE: We sought to construct and evaluate a deep learning (DL) model to diagnose early glaucoma f...

Oct 2018 PMID: 30316669

Development of a deep residual learning algorithm to screen for glaucoma from fundus photography.

The Purpose of the study was to develop a deep residual learning algorithm to screen for glaucoma fr...

Oct 2018 PMID: 30279554

Current state and future prospects of artificial intelligence in ophthalmology: a review.

Artificial intelligence (AI) has emerged as a major frontier in computer science research. Although ...

Sep 2018 PMID: 30155978

Comparison of response surface methodology and artificial neural network to optimize novel ophthalmic flexible nano-liposomes: Characterization, evaluation, in vivo pharmacokinetics and molecular dynamics simulation.

To improve the topical delivery of pilocarpine hydrochloride (PN) to treat glaucoma, flexible nano-l...

Aug 2018 PMID: 30173096

The application of artificial neural networks and support vector regression for simultaneous spectrophotometric determination of commercial eye drop contents.

In the present study, artificial neural networks (ANNs) and support vector regression (SVR) as intel...

Dec 2017 PMID: 29258024

Ophthalmology

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Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color Fundus Images.

Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation.

Can Artificial Intelligence Make Screening Faster, More Accurate, and More Accessible?

Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques.

A deep learning approach to automatic detection of early glaucoma from visual fields.

A deep learning model for the detection of both advanced and early glaucoma using fundus photography.

Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs.

Artificial intelligence and deep learning in ophthalmology.

Using Kalman Filtering to Forecast Disease Trajectory for Patients With Normal Tension Glaucoma.

Using Deep Learning and Transfer Learning to Accurately Diagnose Early-Onset Glaucoma From Macular Optical Coherence Tomography Images.

Development of a deep residual learning algorithm to screen for glaucoma from fundus photography.

Current state and future prospects of artificial intelligence in ophthalmology: a review.

Comparison of response surface methodology and artificial neural network to optimize novel ophthalmic flexible nano-liposomes: Characterization, evaluation, in vivo pharmacokinetics and molecular dynamics simulation.

Use of Machine Learning on Contact Lens Sensor-Derived Parameters for the Diagnosis of Primary Open-angle Glaucoma.

Comparison of Machine-Learning Classification Models for Glaucoma Management.

Detection of Longitudinal Visual Field Progression in Glaucoma Using Machine Learning.

Deep learning in ophthalmology: a review.

Structure-Preserving Guided Retinal Image Filtering and Its Application for Optic Disk Analysis.

Classification of optic disc shape in glaucoma using machine learning based on quantified ocular parameters.

A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis.

The application of artificial neural networks and support vector regression for simultaneous spectrophotometric determination of commercial eye drop contents.

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