AIMC Topic: Retinal Ganglion Cells
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Measurement of retinal nerve fiber layer thickness with a deep learning algorithm in ischemic optic neuropathy and optic neuritis.
Scientific reports
Oct 12, 2022
This work aims at determining the ability of a deep learning (DL) algorithm to measure retinal nerve fiber layer (RNFL) thickness from optical coherence tomography (OCT) scans in anterior ischemic optic neuropathy (NAION) and demyelinating optic neur...
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 ...
Machine learning for comprehensive prediction of high risk for Alzheimer's disease based on chromatic pupilloperimetry.
Scientific reports
Jun 15, 2022
Currently there are no reliable biomarkers for early detection of Alzheimer's disease (AD) at the preclinical stage. This study assessed the pupil light reflex (PLR) for focal red and blue light stimuli in central and peripheral retina in 125 cogniti...
Glaucoma diagnosis using multi-feature analysis and a deep learning technique.
Scientific reports
May 16, 2022
In this study, we aimed to facilitate the current diagnostic assessment of glaucoma by analyzing multiple features and introducing a new cross-sectional optic nerve head (ONH) feature from optical coherence tomography (OCT) images. The data (n = 100 ...
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...
Multimodal Machine Learning Using Visual Fields and Peripapillary Circular OCT Scans in Detection of Glaucomatous Optic Neuropathy.
Ophthalmology
Jul 30, 2021
PURPOSE: To develop and validate a multimodal artificial intelligence algorithm, FusionNet, using the pattern deviation probability plots from visual field (VF) reports and circular peripapillary OCT scans to detect glaucomatous optic neuropathy (GON...
Humans
Machine Learning
Algorithms
Adult
Cross-Sectional Studies
Tomography, Optical Coherence
Female
Nerve Fibers
Aged
ROC Curve
Intraocular Pressure
Retinal Ganglion Cells
Multimodal Imaging
Vision Disorders
Optic Nerve Diseases
Visual Fields
Male
Area Under Curve
Glaucoma, Open-Angle
Middle Aged
Visual Field Tests
Clinically Verified Hybrid Deep Learning System for Retinal Ganglion Cells Aware Grading of Glaucomatous Progression.
IEEE transactions on bio-medical engineering
Jun 17, 2021
OBJECTIVE: Glaucoma is the second leading cause of blindness worldwide. Glaucomatous progression can be easily monitored by analyzing the degeneration of retinal ganglion cells (RGCs). Many researchers have screened glaucoma by measuring cup-to-disc ...
A novel retinal ganglion cell quantification tool based on deep learning.
Scientific reports
Jan 12, 2021
Glaucoma is a disease associated with the loss of retinal ganglion cells (RGCs), and remains one of the primary causes of blindness worldwide. Major research efforts are presently directed towards the understanding of disease pathogenesis and the dev...
Predicting Glaucoma Development With Longitudinal Deep Learning Predictions From Fundus Photographs.
American journal of ophthalmology
Jan 7, 2021
PURPOSE: To assess whether longitudinal changes in a deep learning algorithm's predictions of retinal nerve fiber layer (RNFL) thickness based on fundus photographs can predict future development of glaucomatous visual field defects.
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