AIMC Topic: Retinal Ganglion Cells

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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).
Humans Middle Aged Tomography, Optical Coherence Nerve Fibers Retinal Ganglion Cells Visual Fields Glaucoma Artificial Intelligence Female Aged Male
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Discriminating glaucomatous and compressive optic neuropathy on spectral-domain optical coherence tomography with deep learning classifier.

The British journal of ophthalmology Feb 25, 2020
BACKGROUND/AIMS: To assess the performance of a deep learning classifier for differentiation of glaucomatous optic neuropathy (GON) from compressive optic neuropathy (CON) based on ganglion cell-inner plexiform layer (GCIPL) and retinal nerve fibre l...
Humans Tomography, Optical Coherence Nerve Fibers Optic Disk Retinal Ganglion Cells Visual Fields Optic Nerve Diseases Male Middle Aged Deep Learning ROC Curve Follow-Up Studies Glaucoma Retrospective Studies Female Aged Intraocular Pressure
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Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model.

Scientific reports Feb 24, 2020
We developed a hybrid deep learning model (HDLM) algorithm that quantitatively predicts macular ganglion cell-inner plexiform layer (mGCIPL) thickness from red-free retinal nerve fiber layer photographs (RNFLPs). A total of 789 pairs of RNFLPs and sp...
Intraocular Pressure Male Middle Aged Case-Control Studies Diagnosis, Computer-Assisted Aged Aged, 80 and over Young Adult Algorithms Adult Sensitivity and Specificity Retrospective Studies Tomography, Optical Coherence Female Fundus Oculi Humans Translational Research, Biomedical Glaucoma Retinal Ganglion Cells Deep Learning Diagnostic Techniques, Ophthalmological ROC Curve Refractometry Support Vector Machine Regression Analysis Optic Nerve
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Reconstruction of natural visual scenes from neural spikes with deep neural networks.

Neural networks : the official journal of the International Neural Network Society Feb 8, 2020
Neural coding is one of the central questions in systems neuroscience for understanding how the brain processes stimulus from the environment, moreover, it is also a cornerstone for designing algorithms of brain-machine interface, where decoding inco...
Brain-Computer Interfaces Evoked Potentials, Visual Visual Perception Visual Cortex Neural Networks, Computer Animals Magnetic Resonance Imaging Models, Neurological Retinal Ganglion Cells
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Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie Jan 27, 2020
PURPOSE: To develop a deep learning approach based on deep residual neural network (ResNet101) for the automated detection of glaucomatous optic neuropathy (GON) using color fundus images, understand the process by which the model makes predictions, ...
Diagnostic Techniques, Ophthalmological ROC Curve Tomography, Optical Coherence Optic Disk Retinal Ganglion Cells Optic Nerve Diseases Retrospective Studies Intraocular Pressure Deep Learning Humans Neural Networks, Computer Glaucoma
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Quantification of Retinal Nerve Fibre Layer Thickness on Optical Coherence Tomography with a Deep Learning Segmentation-Free Approach.

Scientific reports Jan 15, 2020
This study describes a segmentation-free deep learning (DL) algorithm for measuring retinal nerve fibre layer (RNFL) thickness on spectral-domain optical coherence tomography (SDOCT). The study included 25,285 B-scans from 1,338 eyes of 706 subjects....
Cross-Sectional Studies Male Middle Aged Case-Control Studies Deep Learning Image Processing, Computer-Assisted Tomography, Optical Coherence Nerve Fibers Retinal Ganglion Cells Visual Fields Humans Glaucoma Algorithms Female
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Improving the Structure-Function Relationship in Glaucomatous Visual Fields by Using a Deep Learning-Based Noise Reduction Approach.

Ophthalmology. Glaucoma Jan 11, 2020
PURPOSE: To investigate whether processing visual field (VF) measurements using a variational autoencoder (VAE) improves the structure-function relationship in glaucoma.
Nerve Fibers Optic Disk Male Retinal Ganglion Cells Humans Middle Aged Visual Fields Female Deep Learning Glaucoma, Open-Angle Cross-Sectional Studies Intraocular Pressure Structure-Activity Relationship Tomography, Optical Coherence
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Machine learning classifiers-based prediction of normal-tension glaucoma progression in young myopic patients.

Japanese journal of ophthalmology Dec 17, 2019
PURPOSE: To assess the performance of machine learning classifiers for prediction of progression of normal-tension glaucoma (NTG) in young myopic patients.
Machine Learning Visual Field Tests Female Gonioscopy Slit Lamp Microscopy Young Adult Low Tension Glaucoma Adult Cross-Sectional Studies ROC Curve Intraocular Pressure Humans Nerve Fibers Area Under Curve Male Retinal Ganglion Cells Tomography, Optical Coherence Disease Progression Visual Fields Myopia Diagnosis, Computer-Assisted
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Detecting glaucoma based on spectral domain optical coherence tomography imaging of peripapillary retinal nerve fiber layer: a comparison study between hand-crafted features and deep learning model.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie Dec 7, 2019
PURPOSE: To develop a deep learning (DL) model for automated detection of glaucoma and to compare diagnostic capability against hand-craft features (HCFs) based on spectral domain optical coherence tomography (SD-OCT) peripapillary retinal nerve fibe...
Young Adult Adult Intraocular Pressure Male Middle Aged Nerve Fibers Optic Disk Retinal Ganglion Cells Deep Learning Adolescent Tomography, Optical Coherence Glaucoma Prospective Studies Female Aged Aged, 80 and over Humans Visual Fields
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Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs.

American journal of ophthalmology Nov 12, 2019
PURPOSE: To compare the diagnostic performance of human gradings vs predictions provided by a machine-to-machine (M2M) deep learning (DL) algorithm trained to quantify retinal nerve fiber layer (RNFL) damage on fundus photographs.
Photography Physical Examination Glaucoma, Open-Angle Gonioscopy Cross-Sectional Studies ROC Curve Humans Intraocular Pressure Algorithms Vision Disorders Male Retrospective Studies Area Under Curve Middle Aged Tomography, Optical Coherence Female Deep Learning Fundus Oculi Aged Nerve Fibers Retinal Ganglion Cells Visual Fields Visual Field Tests Optic Nerve Diseases
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