AIMC Topic: Visual Fields
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Deep learning approaches to predict 10-2 visual field from wide-field swept-source optical coherence tomography en face images in glaucoma.
Scientific reports
Dec 5, 2022
Close monitoring of central visual field (VF) defects with 10-2 VF helps prevent blindness in glaucoma. We aimed to develop a deep learning model to predict 10-2 VF from wide-field swept-source optical coherence tomography (SS-OCT) images. Macular ga...
Deep Learning-Assisted Detection of Glaucoma Progression in Spectral-Domain OCT.
Ophthalmology. Glaucoma
Nov 18, 2022
PURPOSE: To develop and validate a deep learning (DL) model for detection of glaucoma progression using spectral-domain (SD)-OCT measurements of retinal nerve fiber layer (RNFL) thickness.
Deep Learning Estimation of 10-2 Visual Field Map Based on Circumpapillary Retinal Nerve Fiber Layer Thickness Measurements.
American journal of ophthalmology
Nov 1, 2022
PURPOSE: To estimate central 10-degree visual field (VF) map from spectral-domain optical coherence tomography (SD-OCT) retinal nerve fiber layer thickness (RNFL) measurements in glaucoma with artificial intelligence.
Deep Learning-Based Noise Reduction Improves Optical Coherence Tomography Angiography Imaging of Radial Peripapillary Capillaries in Advanced Glaucoma.
Current eye research
Sep 22, 2022
PURPOSE: We applied deep learning-based noise reduction (NR) to optical coherence tomography-angiography (OCTA) images of the radial peripapillary capillaries (RPCs) in eyes with glaucoma and investigated the usefulness of this method as an objective...
A Deep Learning Approach to Improve Retinal Structural Predictions and Aid Glaucoma Neuroprotective Clinical Trial Design.
Ophthalmology. Glaucoma
Aug 28, 2022
PURPOSE: To investigate the efficacy of a deep learning regression method to predict macula ganglion cell-inner plexiform layer (GCIPL) and optic nerve head (ONH) retinal nerve fiber layer (RNFL) thickness for use in glaucoma neuroprotection clinical...
Deep learning: applications in retinal and optic nerve diseases.
Clinical & experimental optometry
Aug 23, 2022
Deep learning (DL) represents a paradigm-shifting, burgeoning field of research with emerging clinical applications in optometry. Unlike traditional programming, which relies on human-set specific rules, DL works by exposing the algorithm to a large ...
Policy-Driven, Multimodal Deep Learning for Predicting Visual Fields from the Optic Disc and OCT Imaging.
Ophthalmology
Feb 21, 2022
PURPOSE: To develop and validate a deep learning (DL) system for predicting each point on visual fields (VFs) from disc and OCT imaging and derive a structure-function mapping.
Predicting Visual Fields From Optical Coherence Tomography via an Ensemble of Deep Representation Learners.
American journal of ophthalmology
Jan 5, 2022
PURPOSE: To develop and validate a deep learning method of predicting visual function from spectral domain optical coherence tomography (SD-OCT)-derived retinal nerve fiber layer thickness (RNFLT) measurements and corresponding SD-OCT images.
Deep Learning Image Analysis of Optical Coherence Tomography Angiography Measured Vessel Density Improves Classification of Healthy and Glaucoma Eyes.
American journal of ophthalmology
Nov 13, 2021
PURPOSE: To compare convolutional neural network (CNN) analysis of en face vessel density images to gradient boosting classifier (GBC) analysis of instrument-provided, feature-based optical coherence tomography angiography (OCTA) vessel density measu...
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