AIMC Topic: Retinal Ganglion Cells
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Modeling a population of retinal ganglion cells with restricted Boltzmann machines.
Scientific reports
Oct 6, 2020
The retina is a complex circuit of the central nervous system whose aim is to encode visual stimuli prior the higher order processing performed in the visual cortex. Due to the importance of its role, modeling the retina to advance in interpreting it...
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.
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.
Neurolight: A Deep Learning Neural Interface for Cortical Visual Prostheses.
International journal of neural systems
Jul 21, 2020
Visual neuroprosthesis, that provide electrical stimulation along several sites of the human visual system, constitute a potential tool for vision restoration for the blind. Scientific and technological progress in the fields of neural engineering an...
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).
NeuroConstruct-based implementation of structured-light stimulated retinal circuitry.
BMC neuroscience
Jun 24, 2020
BACKGROUND: Retinal circuitry provides a fundamental window to neural networks, featuring widely investigated visual phenomena ranging from direction selectivity to fast detection of approaching motion. As the divide between experimental and theoreti...
An innovative method for screening and evaluating the degree of diabetic retinopathy and drug treatment based on artificial intelligence algorithms.
Pharmacological research
Jun 2, 2020
Current methods of evaluating the degree of diabetic retinopathy are highly subjective and have no quantitative standard. To objectively evaluate the slight changes in tissue structure during the early stage of retinal diseases, a subjective interpre...
Predictive Value of Tests
Drugs, Chinese Herbal
Diabetes Mellitus, Experimental
Retina
Mice
Rats, Wistar
Diabetes Mellitus, Type 1
Hypoglycemic Agents
Pattern Recognition, Automated
Retinal Vessels
Microscopy
Image Interpretation, Computer-Assisted
Early Diagnosis
Retinal Ganglion Cells
Diabetic Retinopathy
Animals
Male
Severity of Illness Index
Deep Learning
Diabetes Mellitus, Type 2
AxoNet: A deep learning-based tool to count retinal ganglion cell axons.
Scientific reports
May 15, 2020
In this work, we develop a robust, extensible tool to automatically and accurately count retinal ganglion cell axons in optic nerve (ON) tissue images from various animal models of glaucoma. We adapted deep learning to regress pixelwise axon count de...
A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells).
Expert review of molecular diagnostics
May 3, 2020
BACKGROUND: A key objective in glaucoma is to identify those at risk of rapid progression and blindness. Recently, a novel first-in-man method for visualising apoptotic retinal cells called DARC (Detection-of-Apoptosing-Retinal-Cells) was reported. T...
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.
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