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Glaucoma

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

Ophthalmology. Glaucoma
We hypothesize that artificial intelligence (AI) applied to relevant clinical testing in glaucoma has the potential to enhance the ability to detect glaucoma. This premise was discussed at the recent Collaborative Community on Ophthalmic Imaging meet...

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

American journal of ophthalmology
PURPOSE: To compare convolutional neural network (CNN) analysis of en face vessel density images to gradient boosting classifier (GBC) analysis of instrument-provided, feature-based optical coherence tomography angiography (OCTA) vessel density measu...

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

Computational and mathematical methods in medicine
Glaucoma is a chronic ocular disease characterized by damage to the optic nerve resulting in progressive and irreversible visual loss. Early detection and timely clinical interventions are critical in improving glaucoma-related outcomes. As a typical...

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

Scientific reports
Although unprecedented sensitivity and specificity values are reported, recent glaucoma detection deep learning models lack in decision transparency. Here, we propose a methodology that advances explainable deep learning in the field of glaucoma dete...

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

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

Detection of shallow anterior chamber depth from two-dimensional anterior segment photographs using deep learning.

BMC ophthalmology
BACKGROUND: The purpose of this study was to implement and evaluate a deep learning (DL) approach for automatically detecting shallow anterior chamber depth (ACD) from two-dimensional (2D) overview anterior segment photographs.

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

Journal of medical Internet research
BACKGROUND: Glaucoma leads to irreversible blindness. Globally, it is the second most common retinal disease that leads to blindness, slightly less common than cataracts. Therefore, there is a great need to avoid the silent growth of this disease usi...

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

IEEE journal of biomedical and health informatics
With the popularization of computer-aided diagnosis (CAD) technologies, more and more deep learning methods are developed to facilitate the detection of ophthalmic diseases. In this article, the deep learning-based detections for some common eye dise...

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

Nature communications
Retinal fundus diseases can lead to irreversible visual impairment without timely diagnoses and appropriate treatments. Single disease-based deep learning algorithms had been developed for the detection of diabetic retinopathy, age-related macular de...