AIMC Topic: Visual Fields

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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....
Humans Glaucoma Algorithms Deep Learning Female Image Processing, Computer-Assisted Cross-Sectional Studies Tomography, Optical Coherence Male Nerve Fibers Middle Aged Retinal Ganglion Cells Case-Control Studies Visual Fields
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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.
Visual Fields Glaucoma, Open-Angle Humans Structure-Activity Relationship Female Tomography, Optical Coherence Cross-Sectional Studies Nerve Fibers Intraocular Pressure Optic Disk Male Retinal Ganglion Cells Middle Aged Deep Learning
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Beyond Performance Metrics: Automatic Deep Learning Retinal OCT Analysis Reproduces Clinical Trial Outcome.

Ophthalmology Dec 23, 2019
PURPOSE: To validate the efficacy of a fully automatic, deep learning-based segmentation algorithm beyond conventional performance metrics by measuring the primary outcome of a clinical trial for macular telangiectasia type 2 (MacTel2).
Retinal Vessels Retinal Telangiectasis Drug Implants Retinal Photoreceptor Cell Inner Segment Retinal Photoreceptor Cell Outer Segment Tomography, Optical Coherence Visual Fields Visual Field Tests Humans Fluorescein Angiography Female Ciliary Neurotrophic Factor Reproducibility of Results Male Deep Learning Treatment Outcome Visual Acuity
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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.
Diagnosis, Computer-Assisted ROC Curve Area Under Curve Tomography, Optical Coherence Nerve Fibers Cross-Sectional Studies Intraocular Pressure Male Disease Progression Myopia Low Tension Glaucoma Retinal Ganglion Cells Visual Fields Visual Field Tests Gonioscopy Slit Lamp Microscopy Humans Machine Learning Female Young Adult Adult
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Artificial Intelligence Classification of Central Visual Field Patterns in Glaucoma.

Ophthalmology Dec 12, 2019
PURPOSE: To quantify the central visual field (VF) loss patterns in glaucoma using artificial intelligence.
Intraocular Pressure Male Middle Aged Vision Disorders Bayes Theorem Artificial Intelligence Retrospective Studies Female Visual Fields Aged Humans Cross-Sectional Studies Visual Field Tests Glaucoma
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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...
Humans Glaucoma Prospective Studies Female Aged Optic Disk Retinal Ganglion Cells Visual Fields Middle Aged Deep Learning Aged, 80 and over Adolescent Young Adult Tomography, Optical Coherence Adult Nerve Fibers Intraocular Pressure Male
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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.
Humans Algorithms Retrospective Studies Female Aged Nerve Fibers ROC Curve Retinal Ganglion Cells Gonioscopy Vision Disorders Cross-Sectional Studies Photography Visual Fields Area Under Curve Intraocular Pressure Physical Examination Visual Field Tests Tomography, Optical Coherence Male Glaucoma, Open-Angle Optic Nerve Diseases Fundus Oculi Middle Aged Deep Learning
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Forecasting Retinal Nerve Fiber Layer Thickness from Multimodal Temporal Data Incorporating OCT Volumes.

Ophthalmology. Glaucoma Nov 8, 2019
PURPOSE: The purpose of this study was to develop a machine learning model to forecast future circumpapillary retinal nerve fiber layer (cpRNFL) thickness in eyes of healthy, glaucoma suspect, and glaucoma participants from multimodal temporal data.
Male Aged Humans Middle Aged Optic Disk Glaucoma Aged, 80 and over Follow-Up Studies Retinal Ganglion Cells Forecasting Young Adult Tomography, Optical Coherence Visual Fields Retrospective Studies Adult Nerve Fibers Female Intraocular Pressure
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Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps.

Ophthalmology Sep 30, 2019
PURPOSE: To develop and evaluate a deep learning system for differentiating between eyes with and without glaucomatous visual field damage (GVFD) and predicting the severity of GFVD from spectral domain OCT (SD OCT) optic nerve head images.
Visual Field Tests Optic Nerve Diseases Diagnostic Techniques, Ophthalmological Predictive Value of Tests Nerve Fibers Optic Disk Retinal Ganglion Cells Visual Fields Aged Adult Middle Aged Humans Male Deep Learning Glaucoma Vision Disorders Female
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The impact of artificial intelligence in the diagnosis and management of glaucoma.

Eye (London, England) Sep 20, 2019
Deep learning (DL) is a subset of artificial intelligence (AI), which uses multilayer neural networks modelled after the mammalian visual cortex capable of synthesizing images in ways that will transform the field of glaucoma. Autonomous DL algorithm...
Artificial Intelligence Algorithms Intraocular Pressure Animals Visual Fields Humans Glaucoma
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