AIMC Topic: Visual Fields

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Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps.

Ophthalmology Sep 30, 2019
PURPOSE: To develop and evaluate a deep learning system for differentiating between eyes with and without glaucomatous visual field damage (GVFD) and predicting the severity of GFVD from spectral domain OCT (SD OCT) optic nerve head images.
Nerve Fibers Optic Disk Retinal Ganglion Cells Visual Fields Visual Field Tests Middle Aged Deep Learning Vision Disorders Predictive Value of Tests Optic Nerve Diseases Diagnostic Techniques, Ophthalmological Humans Glaucoma Female Aged Adult Male
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The impact of artificial intelligence in the diagnosis and management of glaucoma.

Eye (London, England) Sep 20, 2019
Deep learning (DL) is a subset of artificial intelligence (AI), which uses multilayer neural networks modelled after the mammalian visual cortex capable of synthesizing images in ways that will transform the field of glaucoma. Autonomous DL algorithm...
Artificial Intelligence Algorithms Intraocular Pressure Humans Animals Glaucoma Visual Fields
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Machine Learning Models for Diagnosing Glaucoma from Retinal Nerve Fiber Layer Thickness Maps.

Ophthalmology. Glaucoma Aug 23, 2019
PURPOSE: To assess the diagnostic accuracy of multiple machine learning models using full retinal nerve fiber layer (RNFL) thickness maps in detecting glaucoma.
Humans Optic Disk Retinal Ganglion Cells Visual Fields Glaucoma Intraocular Pressure Machine Learning Male Female Case-Control Studies Aged Tomography, Optical Coherence Nerve Fibers
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Machine Learning in the Detection of the Glaucomatous Disc and Visual Field.

Seminars in ophthalmology May 27, 2019
Glaucoma is the leading cause of irreversible blindness worldwide. Early detection is of utmost importance as there is abundant evidence that early treatment prevents disease progression, preserves vision, and improves patients' long-term quality of ...
Visual Field Tests Machine Learning Optic Nerve Diseases Humans Diagnosis, Computer-Assisted Diagnostic Techniques, Ophthalmological Glaucoma Vision Disorders Visual Fields
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Forecasting future Humphrey Visual Fields using deep learning.

PloS one Apr 5, 2019
PURPOSE: To determine if deep learning networks could be trained to forecast future 24-2 Humphrey Visual Fields (HVFs).
Disease Progression Spatio-Temporal Analysis Deep Learning Models, Statistical Visual Fields Linear Models Visual Field Tests Humans Glaucoma Aged Algorithms Adult Forecasting Male Databases, Factual Middle Aged Female
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Patient-attentive sequential strategy for perimetry-based visual field acquisition.

Medical image analysis Mar 23, 2019
Perimetry is a non-invasive clinical psychometric examination used for diagnosing ophthalmic and neurological conditions. At its core, perimetry relies on a subject pressing a button whenever they see a visual stimulus within their field of view. Thi...
Glaucoma Algorithms Psychometrics Visual Fields Visual Field Tests Humans Neural Networks, Computer Monte Carlo Method
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Posterior scleral deformations around optic disc are associated with visual field damage in open-angle glaucoma patients with myopia.

PloS one Mar 15, 2019
PURPOSE: To identify important variables associated with visual field (VF) defects in open-angle glaucoma (OAG) with myopia.
Humans Logistic Models Neural Networks, Computer Animals Optic Disk Sclera Glaucoma Male Visual Fields Middle Aged Adult Visual Field Tests Myopia Cross-Sectional Studies Mice Intraocular Pressure
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Feasibility of simple machine learning approaches to support detection of non-glaucomatous visual fields in future automated glaucoma clinics.

Eye (London, England) Mar 4, 2019
OBJECTIVES: To assess the performance of feed-forward back-propagation artificial neural networks (ANNs) in detecting field defects caused by pituitary disease from among a glaucomatous population.
Machine Learning Feasibility Studies Visual Fields Visual Field Tests Humans Neural Networks, Computer Glaucoma
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A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs.

American journal of ophthalmology Jan 26, 2019
PURPOSE: To train a deep learning (DL) algorithm that quantifies glaucomatous neuroretinal damage on fundus photographs using the minimum rim width relative to Bruch membrane opening (BMO-MRW) from spectral-domain optical coherence tomography (SDOCT)...
Glaucoma, Open-Angle Bruch Membrane Area Under Curve Tomography, Optical Coherence Nerve Fibers Optic Disk Retinal Ganglion Cells Visual Fields Visual Field Tests Optic Nerve Diseases Photography Male Middle Aged Deep Learning Vision Disorders Aged Humans Aged, 80 and over Algorithms Cross-Sectional Studies Retrospective Studies Intraocular Pressure Female
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From Machine to Machine: An OCT-Trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs.

Ophthalmology Dec 20, 2018
PURPOSE: Previous approaches using deep learning (DL) algorithms to classify glaucomatous damage on fundus photographs have been limited by the requirement for human labeling of a reference training set. We propose a new approach using quantitative s...
Glaucoma, Open-Angle Middle Aged Aged, 80 and over Tonometry, Ocular Gonioscopy Tomography, Optical Coherence Deep Learning Cross-Sectional Studies Fundus Oculi ROC Curve Visual Fields Intraocular Pressure Nerve Fibers Visual Field Tests Visual Acuity Male Humans Optic Disk Optic Nerve Diseases Retinal Ganglion Cells Algorithms Photography Female Aged
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