Glaucoma

Evaluation of machine learning algorithms for trabeculectomy outcome prediction in patients with glaucoma.

The purpose of this study was to evaluate the performance of machine learning algorithms to predict trabeculectomy surgical outcomes. Preoperative systemic, demographic and ocular data from consecutive trabeculectomy surgeries from a single academic institution between January 2014 and December 2018 were incorporated into models using random forest, support vector machine, artificial neural networ...

Evaluations of Deep Learning Approaches for Glaucoma Screening Using Retinal Images from Mobile Device.

Glaucoma is a silent disease that leads to vision loss or irreversible blindness. Current deep learning methods can help glaucoma screening by extending it to larger populations using retinal images. Low-cost lenses attached to mobile devices can increase the frequency of screening and alert patients earlier for a more thorough evaluation. This work explored and compared the performance of classif...

Glaucoma disease diagnosis with an artificial algae-based deep learning algorithm.

Glaucoma disease is optic neuropathy; in glaucoma, the optic nerve is damaged because the long duration of intraocular pressure can be caused blindnes...

Microscopic retinal blood vessels detection and segmentation using support vector machine and K-nearest neighbors.

The retina is the deepest layer of texture covering the rear of the eye, recorded by fundus images. Vessel detection and segmentation are useful in di...

An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization.

Glaucoma is an eye disease initiated due to excessive intraocular pressure inside it and caused complete sightlessness at its progressed stage. Wherea...

[Artificial intelligence and glaucoma: A literature review].

In recent years, research in artificial intelligence (AI) has experienced an unprecedented surge in the field of ophthalmology, in particular glaucoma...

A Case for the Use of Artificial Intelligence in Glaucoma Assessment.

We hypothesize that artificial intelligence (AI) applied to relevant clinical testing in glaucoma has the potential to enhance the ability to detect g...

Augmenting Kalman Filter Machine Learning Models with Data from OCT to Predict Future Visual Field Loss: An Analysis Using Data from the African Descent and Glaucoma Evaluation Study and the Diagnostic Innovation in Glaucoma Study.

PURPOSE: To assess whether the predictive accuracy of machine learning algorithms using Kalman filtering for forecasting future values of global indic...

Comment on: Predicting Glaucoma Development With Longitudinal Deep Learning Predictions From Fundus Photographs.

Open-source deep learning-based automatic segmentation of mouse Schlemm's canal in optical coherence tomography images.

The purpose of this study was to develop an automatic deep learning-based approach and corresponding free, open-source software to perform segmentatio...

Deep Learning Image Analysis of Optical Coherence Tomography Angiography Measured Vessel Density Improves Classification of Healthy and Glaucoma Eyes.

PURPOSE: To compare convolutional neural network (CNN) analysis of en face vessel density images to gradient boosting classifier (GBC) analysis of ins...

A Novel Context Aware Joint Segmentation and Classification Framework for Glaucoma Detection.

Glaucoma is a chronic ocular disease characterized by damage to the optic nerve resulting in progressive and irreversible visual loss. Early detection...

Deep learning on fundus images detects glaucoma beyond the optic disc.

Although unprecedented sensitivity and specificity values are reported, recent glaucoma detection deep learning models lack in decision transparency. ...

Deep learning versus ophthalmologists for screening for glaucoma on fundus examination: A systematic review and meta-analysis.

BACKGROUND: In this systematic review and meta-analysis, we aimed to compare deep learning versus ophthalmologists in glaucoma diagnosis on fundus exa...

Accuracy of Using Generative Adversarial Networks for Glaucoma Detection: Systematic Review and Bibliometric Analysis.

BACKGROUND: Glaucoma leads to irreversible blindness. Globally, it is the second most common retinal disease that leads to blindness, slightly less co...

Ophthalmic Disease Detection via Deep Learning With a Novel Mixture Loss Function.

With the popularization of computer-aided diagnosis (CAD) technologies, more and more deep learning methods are developed to facilitate the detection ...

Fast and Accurate Ophthalmic Medication Bottle Identification Using Deep Learning on a Smartphone Device.

PURPOSE: To assess the accuracy and efficacy of deep learning models, specifically convolutional neural networks (CNNs), to identify glaucoma medicati...

Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks.

Retinal fundus diseases can lead to irreversible visual impairment without timely diagnoses and appropriate treatments. Single disease-based deep lear...

Emergence of non-artificial intelligence digital health innovations in ophthalmology: A systematic review.

The prominent rise of digital health in ophthalmology is evident in the current age of Industry 4.0. Despite the many facets of digital health, there ...

Deep learning-assisted (automatic) diagnosis of glaucoma using a smartphone.

BACKGROUND/AIMS: To validate a deep learning algorithm to diagnose glaucoma from fundus photography obtained with a smartphone.