AIMC Topic: Visual Fields

Clear Filters Showing 51 to 60 of 120 articles

Estimating visual field loss from monoscopic optic disc photography using deep learning model.

Scientific reports Dec 3, 2020
Visual field assessment is recognized as the important criterion of glaucomatous damage judgement; however, it can show large test-retest variability. We developed a deep learning (DL) algorithm that quantitatively predicts mean deviation (MD) of sta...
Humans Algorithms Female Models, Biological Optic Disk Visual Fields Photography Male Middle Aged Deep Learning ROC Curve
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Detection of Glaucoma Deterioration in the Macular Region with Optical Coherence Tomography: Challenges and Solutions.

American journal of ophthalmology Sep 18, 2020
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...
Macula Lutea Tomography, Optical Coherence Nerve Fibers Visual Fields Humans Glaucoma Artificial Intelligence Intraocular Pressure
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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.
Glaucoma Artificial Intelligence Nerve Fibers Retinal Ganglion Cells Humans Visual Fields
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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.
Nerve Fibers Retinal Ganglion Cells Visual Fields Visual Field Tests Cross-Sectional Studies Intraocular Pressure Deep Learning Tomography, Optical Coherence Humans Algorithms
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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).
Predictive Value of Tests Tomography, Optical Coherence Nerve Fibers Retinal Ganglion Cells Male Middle Aged Deep Learning Visual Fields Visual Field Tests Gonioscopy Humans Glaucoma Female Aged Cross-Sectional Studies 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 Perception Visual Cortex Neurons Visual Fields Normal Distribution Photic Stimulation Humans Neural Networks, Computer Eye Movements
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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...
Sensitivity and Specificity Intraocular Pressure Male Middle Aged Disease Progression Deep Learning Visual Fields Linear Models Humans Female Reproducibility of Results Adult Visual Field Tests Eye Glaucoma, Open-Angle
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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.
Slit Lamp Microscopy Axial Length, Eye Visual Field Tests Optic Nerve Diseases Glaucoma, Open-Angle Gonioscopy Vision Disorders Tomography, Optical Coherence Visual Fields Adult Sensitivity and Specificity Cross-Sectional Studies Intraocular Pressure Male Middle Aged Deep Learning Visual Acuity Humans Retrospective Studies Female Aged
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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.
Tomography, Optical Coherence Retinal Ganglion Cells Visual Fields Humans Male Middle Aged Deep Learning Predictive Value of Tests Glaucoma Prospective Studies Female Intraocular Pressure
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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).
Nerve Fibers Retinal Ganglion Cells Visual Fields Male Middle Aged Tomography, Optical Coherence Humans Glaucoma Artificial Intelligence Female Aged
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