Glaucoma

Glaucoma diagnosis using multi-feature analysis and a deep learning technique.

In this study, we aimed to facilitate the current diagnostic assessment of glaucoma by analyzing multiple features and introducing a new cross-sectional optic nerve head (ONH) feature from optical coherence tomography (OCT) images. The data (n = 100 for both glaucoma and control) were collected based on structural, functional, demographic and risk factors. The features were statistically analyzed,...

Deep Learning for Ocular Disease Recognition: An Inner-Class Balance.

It can be challenging for doctors to identify eye disorders early enough using fundus pictures. Diagnosing ocular illnesses by hand is time-consuming, error-prone, and complicated. Therefore, an automated ocular disease detection system with computer-aided tools is necessary to detect various eye disorders using fundus pictures. Such a system is now possible as a consequence of deep learning algor...

Joint optic disk and cup segmentation for glaucoma screening using a region-based deep learning network.

OBJECTIVES: To develop and validate an end-to-end region-based deep convolutional neural network (R-DCNN) to jointly segment the optic disc (OD) and o...

Diagnostic Accuracy of Artificial Intelligence in Glaucoma Screening and Clinical Practice.

PURPOSE: Artificial intelligence (AI) has been shown as a diagnostic tool for glaucoma detection through imaging modalities. However, these tools are ...

A deep learning approach for detection of shallow anterior chamber depth based on the hidden features of fundus photographs.

BACKGROUND AND OBJECTIVES: Patients with angle-closure glaucoma (ACG) are asymptomatic until they experience a painful attack. Shallow anterior chambe...

Artificial Intelligence for Glaucoma: Creating and Implementing Artificial Intelligence for Disease Detection and Progression.

On September 3, 2020, the Collaborative Community on Ophthalmic Imaging conducted its first 2-day virtual workshop on the role of artificial intellige...

Extraction of Active Medications and Adherence Using Natural Language Processing for Glaucoma Patients.

Accuracy of medication data in electronic health records (EHRs) is crucial for patient care and research, but many studies have shown that medication ...

Segmentation and Classification of Glaucoma Using U-Net with Deep Learning Model.

Glaucoma is the second most common cause for blindness around the world and the third most common in Europe and the USA. Around 78 million people are ...

Detection of microscopic glaucoma through fundus images using deep transfer learning approach.

Glaucoma disease in humans can lead to blindness if it progresses to the point where it affects the oculus' optic nerve head. It is not easily detecte...

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 sys...

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 extendi...

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...