AIMC Topic: Glaucoma

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Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice.

Translational vision science & technology
PURPOSE: This concise review aims to explore the potential for the clinical implementation of artificial intelligence (AI) strategies for detecting glaucoma and monitoring glaucoma progression.

Deep learning in glaucoma with optical coherence tomography: a review.

Eye (London, England)
Deep learning (DL), a subset of artificial intelligence (AI) based on deep neural networks, has made significant breakthroughs in medical imaging, particularly for image classification and pattern recognition. In ophthalmology, applying DL for glauco...

Deep-learning-based enhanced optic-disc photography.

PloS one
Optic-disc photography (ODP) has proven to be very useful for optic nerve evaluation in glaucoma. In real clinical practice, however, limited patient cooperation, small pupils, or media opacities can limit the performance of ODP. The purpose of this ...

Detection of Glaucoma Deterioration in the Macular Region with Optical Coherence Tomography: Challenges and Solutions.

American journal of ophthalmology
PURPOSE: Macular imaging with optical coherence tomography (OCT) measures the most critical retinal ganglion cells (RGCs) in the human eye. The goal of this perspective is to review the challenges to detection of glaucoma progression with macular OCT...

[Artificial intelligence for eye care].

Nederlands tijdschrift voor geneeskunde
Technological developments in ophthalmic imaging and artificial intelligence (AI) create new possibilities for diagnostics in eye care. AI has already been applied in ophthalmic diabetes care. AI-systems currently detect diabetic retinopathy in gener...

Deep learning for automated glaucomatous optic neuropathy detection from ultra-widefield fundus images.

The British journal of ophthalmology
BACKGROUND/AIMS: To develop a deep learning system for automated glaucomatous optic neuropathy (GON) detection using ultra-widefield fundus (UWF) images.

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

Translational vision science & technology
PURPOSE: The purpose of this study was to classify the spatial patterns of retinal nerve fiber layer thickness (RNFLT) and assess their associations with visual field (VF) loss in glaucoma.

The role of neuroinflammation in the pathogenesis of glaucoma neurodegeneration.

Progress in brain research
The chapter is a review enclosed in the volume "Glaucoma: A pancitopatia of the retina and beyond." No cure exists for glaucoma. Knowledge on the molecular and cellular alterations underlying glaucoma neurodegeneration (GL-ND) includes innovative and...

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

Progress in brain research
Over the past few years, there has been an unprecedented and tremendous excitement for artificial intelligence (AI) research in the field of Ophthalmology; this has naturally been translated to glaucoma-a progressive optic neuropathy characterized by...