AIMC Topic: Visual Fields

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

Translational vision science & technology Aug 27, 2020
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.
Nerve Fibers Retinal Ganglion Cells Visual Fields Humans Glaucoma Artificial Intelligence
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Prediction of visual field from swept-source optical coherence tomography using deep learning algorithms.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie Aug 26, 2020
PURPOSE: To develop a deep learning method to predict visual field (VF) from wide-angle swept-source optical coherence tomography (SS-OCT) and compare the performance of three Google Inception architectures.
Humans Tomography, Optical Coherence Nerve Fibers Retinal Ganglion Cells Visual Fields Visual Field Tests Algorithms Cross-Sectional Studies Intraocular Pressure Deep Learning
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Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma.

The British journal of ophthalmology Jun 27, 2020
BACKGROUND/AIM: To train and validate the prediction performance of the deep learning (DL) model to predict visual field (VF) in central 10° from spectral domain optical coherence tomography (SD-OCT).
Gonioscopy Male Middle Aged Deep Learning Predictive Value of Tests Female Retinal Ganglion Cells Tomography, Optical Coherence Aged Humans Visual Fields Glaucoma Cross-Sectional Studies Nerve Fibers Visual Field Tests Intraocular Pressure
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A Modeling Study of the Emergence of Eye Position Gain Fields Modulating the Responses of Visual Neurons in the Brain.

Frontiers in neural circuits May 26, 2020
The responses of many cortical neurons to visual stimuli are modulated by the position of the eye. This form of gain modulation by eye position does not change the retinotopic selectivity of the responses, but only changes the amplitude of the respon...
Visual Cortex Visual Fields Humans Normal Distribution Neural Networks, Computer Photic Stimulation Eye Movements Visual Perception Neurons
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The usefulness of the Deep Learning method of variational autoencoder to reduce measurement noise in glaucomatous visual fields.

Scientific reports May 12, 2020
The aim of the study was to investigate the usefulness of processing visual field (VF) using a variational autoencoder (VAE). The training data consisted of 82,433 VFs from 16,836 eyes. Testing dataset 1 consisted of test-retest VFs from 104 eyes wit...
Disease Progression Deep Learning Visual Fields Linear Models Glaucoma, Open-Angle Visual Field Tests Eye Sensitivity and Specificity Intraocular Pressure Male Middle Aged Humans Female Reproducibility of Results Adult
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Predicting the Glaucomatous Central 10-Degree Visual Field From Optical Coherence Tomography Using Deep Learning and Tensor Regression.

American journal of ophthalmology May 6, 2020
PURPOSE: To predict the visual field (VF) of glaucoma patients within the central 10° from optical coherence tomography (OCT) measurements using deep learning and tensor regression.
Cross-Sectional Studies Intraocular Pressure Male Middle Aged Female Aged Adult Sensitivity and Specificity Visual Fields Visual Field Tests Optic Nerve Diseases Glaucoma, Open-Angle Gonioscopy Deep Learning Visual Acuity Vision Disorders Tomography, Optical Coherence Humans Retrospective Studies Slit Lamp Microscopy Axial Length, Eye
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Predicting Glaucoma before Onset Using Deep Learning.

Ophthalmology. Glaucoma Apr 29, 2020
PURPOSE: To assess the accuracy of deep learning models to predict glaucoma development from fundus photographs several years before disease onset.
Glaucoma Prospective Studies Female Intraocular Pressure Humans Tomography, Optical Coherence Retinal Ganglion Cells Visual Fields Male Middle Aged Deep Learning Predictive Value of Tests
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Artificial Intelligence Mapping of Structure to Function in Glaucoma.

Translational vision science & technology Mar 30, 2020
PURPOSE: To develop an artificial intelligence (AI)-based structure-function (SF) map relating retinal nerve fiber layer (RNFL) damage on spectral domain optical coherence tomography (SDOCT) to functional loss on standard automated perimetry (SAP).
Humans Nerve Fibers Male Retinal Ganglion Cells Female Middle Aged Glaucoma Visual Fields Aged Tomography, Optical Coherence Artificial Intelligence
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Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard.

Ophthalmology Mar 10, 2020
PURPOSE: To develop an artificial intelligence (AI) dashboard for monitoring glaucomatous functional loss.
Humans Glaucoma Middle Aged Longitudinal Studies Monitoring, Physiologic Visual Acuity Visual Fields False Negative Reactions Optic Nerve Diseases Vision Disorders Adult Predictive Value of Tests Sensitivity and Specificity Artificial Intelligence Cross-Sectional Studies Retrospective Studies Male Female Aged
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Discriminating glaucomatous and compressive optic neuropathy on spectral-domain optical coherence tomography with deep learning classifier.

The British journal of ophthalmology Feb 25, 2020
BACKGROUND/AIMS: To assess the performance of a deep learning classifier for differentiation of glaucomatous optic neuropathy (GON) from compressive optic neuropathy (CON) based on ganglion cell-inner plexiform layer (GCIPL) and retinal nerve fibre l...
Follow-Up Studies Humans Tomography, Optical Coherence Glaucoma Nerve Fibers Optic Disk Optic Nerve Diseases Retinal Ganglion Cells Visual Fields Retrospective Studies Male Female Aged Middle Aged Intraocular Pressure Deep Learning ROC Curve
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