AIMC Topic: 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 Glaucoma Machine Learning Female Tomography, Optical Coherence Nerve Fibers Optic Disk Retinal Ganglion Cells Visual Fields Aged Intraocular Pressure Male Case-Control Studies
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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 ...
Humans Glaucoma Machine Learning Diagnosis, Computer-Assisted Diagnostic Techniques, Ophthalmological Vision Disorders Visual Fields Visual Field Tests Optic Nerve Diseases
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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).
Models, Statistical Visual Fields Linear Models Visual Field Tests Databases, Factual Humans Female Glaucoma Aged Algorithms Adult Male Forecasting Middle Aged Disease Progression Spatio-Temporal Analysis Deep Learning
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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...
Neural Networks, Computer Glaucoma Algorithms Psychometrics Visual Fields Visual Field Tests Monte Carlo Method Humans
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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.
Visual Fields Visual Field Tests Sclera Cross-Sectional Studies Intraocular Pressure Animals Male Middle Aged Myopia Mice Logistic Models Optic Disk Humans Neural Networks, Computer Glaucoma Adult
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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.
Visual Field Tests Glaucoma Machine Learning Feasibility Studies Visual Fields Humans Neural Networks, Computer
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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)...
Algorithms Retrospective Studies Female Aged Aged, 80 and over Humans Deep Learning Vision Disorders Area Under Curve Tomography, Optical Coherence Nerve Fibers Cross-Sectional Studies Intraocular Pressure Male Middle Aged Optic Nerve Diseases Optic Disk Photography Retinal Ganglion Cells Glaucoma, Open-Angle Visual Fields Bruch Membrane Visual Field Tests
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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...
Optic Nerve Diseases Humans Photography Glaucoma, Open-Angle Algorithms Intraocular Pressure Gonioscopy Tonometry, Ocular Optic Disk Visual Acuity Male Female Retinal Ganglion Cells Tomography, Optical Coherence Middle Aged Aged Visual Fields Fundus Oculi Deep Learning Aged, 80 and over Visual Field Tests Nerve Fibers ROC Curve Cross-Sectional Studies
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A deep learning approach to automatic detection of early glaucoma from visual fields.

PloS one Nov 28, 2018
PURPOSE: To investigate the suitability of multi-scale spatial information in 30o visual fields (VF), computed from a Convolutional Neural Network (CNN) classifier, for early-glaucoma vs. control discrimination.
Databases as Topic Deep Learning Humans Image Processing, Computer-Assisted Neural Networks, Computer Early Diagnosis Glaucoma Visual Fields Algorithms
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Using Kalman Filtering to Forecast Disease Trajectory for Patients With Normal Tension Glaucoma.

American journal of ophthalmology Oct 16, 2018
PURPOSE: To determine whether a machine learning technique called Kalman filtering (KF) can accurately forecast future values of mean deviation (MD), pattern standard deviation, and intraocular pressure for patients with normal tension glaucoma (NTG)...
Visual Fields Forecasting Visual Field Tests Humans Male Retrospective Studies Low Tension Glaucoma Machine Learning Middle Aged Algorithms Female Asian People Aged Japan Intraocular Pressure
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