AIMC Topic: Visual Fields
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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.
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)...
Using Deep Learning and Transfer Learning to Accurately Diagnose Early-Onset Glaucoma From Macular Optical Coherence Tomography Images.
American journal of ophthalmology
Oct 12, 2018
PURPOSE: We sought to construct and evaluate a deep learning (DL) model to diagnose early glaucoma from spectral-domain optical coherence tomography (OCT) images.
Aged
Adult
Sensitivity and Specificity
Intraocular Pressure
Male
Humans
Glaucoma
Female
Reproducibility of Results
Visual Fields
Visual Field Tests
Transfer, Psychology
Glaucoma, Open-Angle
Gonioscopy
Tomography, Optical Coherence
Datasets as Topic
Nerve Fibers
Retinal Ganglion Cells
Middle Aged
Deep Learning
ROC Curve
Area Under Curve
Detection of Longitudinal Visual Field Progression in Glaucoma Using Machine Learning.
American journal of ophthalmology
Jun 18, 2018
PURPOSE: Global indices of standard automated perimerty are insensitive to localized losses, while point-wise indices are sensitive but highly variable. Region-wise indices sit in between. This study introduces a machine learning-based index for glau...
Computational mechanisms underlying cortical responses to the affordance properties of visual scenes.
PLoS computational biology
Apr 23, 2018
Biologically inspired deep convolutional neural networks (CNNs), trained for computer vision tasks, have been found to predict cortical responses with remarkable accuracy. However, the internal operations of these models remain poorly understood, and...
Predictions of ocular changes caused by diabetes in glaucoma patients.
Computer methods and programs in biomedicine
Nov 15, 2017
BACKGROUND AND OBJECTIVE: This paper builds different neural network models with simple topologies, having one or two hidden layers which were subsequently employed in the prediction of ocular changes progression in patients with diabetes associated ...
A neural model of the frontal eye fields with reward-based learning.
Neural networks : the official journal of the International Neural Network Society
May 24, 2016
Decision-making is a flexible process dependent on the accumulation of various kinds of information; however, the corresponding neural mechanisms are far from clear. We extended a layered model of the frontal eye field to a learning-based model, usin...
Transient but significant visual field defects after robot-assisted laparoscopic radical prostatectomy in deep tRendelenburg position.
PloS one
Apr 23, 2015
BACKGROUND: Robot-assisted laparoscopic radical prostatectomy (RALP) is a minimally invasive surgical procedure for prostate cancer. During RALP, the patient must be in a steep Trendelenburg (head-down) position, which leads to a significant increase...
High-Accuracy Digitization of Humphrey Visual Field Reports Using Convolutional Neural Networks.
Translational vision science & technology
May 1, 2025
PURPOSE: Glaucoma is a leading cause of irreversible blindness worldwide, necessitating precise visual field (VF) assessments for effective diagnosis and management. The ability to accurately digitize VF reports is critical for maximizing the utility...
Structure-Function Correlation of Deep-Learning Quantified Ellipsoid Zone and Retinal Pigment Epithelium Loss and Microperimetry in Geographic Atrophy.
Investigative ophthalmology & visual science
Mar 3, 2025
PURPOSE: The purpose of this study was to define structure-function correlation of geographic atrophy (GA) on optical coherence tomography (OCT) and functional testing on microperimetry (MP) based on deep-learning (DL)-quantified spectral-domain OCT ...
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