AIMC Topic: Visual Fields

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Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard.

Ophthalmology Mar 10, 2020
PURPOSE: To develop an artificial intelligence (AI) dashboard for monitoring glaucomatous functional loss.
Artificial Intelligence Retrospective Studies Female Aged Adult Sensitivity and Specificity Humans Glaucoma Visual Acuity Vision Disorders Predictive Value of Tests Visual Fields False Negative Reactions Optic Nerve Diseases Cross-Sectional Studies Male Middle Aged Longitudinal Studies Monitoring, Physiologic
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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...
Retinal Ganglion Cells Visual Fields Optic Nerve Diseases Female Aged Intraocular Pressure Male Middle Aged Deep Learning ROC Curve Follow-Up Studies Tomography, Optical Coherence Nerve Fibers Optic Disk Humans Glaucoma Retrospective Studies
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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 Female Cross-Sectional Studies Male Nerve Fibers Retinal Ganglion Cells Visual Fields Middle Aged Case-Control Studies Deep Learning Image Processing, Computer-Assisted Tomography, Optical Coherence
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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.
Intraocular Pressure Male Middle Aged Deep Learning Structure-Activity Relationship Tomography, Optical Coherence Humans Female Cross-Sectional Studies Nerve Fibers Optic Disk Retinal Ganglion Cells Visual Fields Glaucoma, Open-Angle
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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).
Humans Female Reproducibility of Results Male Deep Learning Treatment Outcome Visual Acuity Tomography, Optical Coherence Visual Fields Visual Field Tests Retinal Photoreceptor Cell Inner Segment Fluorescein Angiography Retinal Photoreceptor Cell Outer Segment Ciliary Neurotrophic Factor Retinal Vessels Retinal Telangiectasis Drug Implants
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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.
Low Tension Glaucoma Retinal Ganglion Cells Adult Visual Fields Cross-Sectional Studies Visual Field Tests Intraocular Pressure Gonioscopy Male Slit Lamp Microscopy Disease Progression Myopia Diagnosis, Computer-Assisted Humans ROC Curve Area Under Curve Machine Learning Tomography, Optical Coherence Female Nerve Fibers Young 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.
Middle Aged Vision Disorders Bayes Theorem Visual Fields Visual Field Tests Humans Female Glaucoma Aged Artificial Intelligence Cross-Sectional Studies Retrospective Studies Intraocular Pressure Male
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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...
Retinal Ganglion Cells Visual Fields Aged, 80 and over Young Adult Adult Intraocular Pressure Male Middle Aged Deep Learning Adolescent Tomography, Optical Coherence Humans Nerve Fibers Glaucoma Optic Disk Prospective Studies Female Aged
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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.
Gonioscopy Visual Fields Visual Field Tests Optic Nerve Diseases Photography Physical Examination Glaucoma, Open-Angle Area Under Curve Tomography, Optical Coherence Fundus Oculi Nerve Fibers Retinal Ganglion Cells Humans Intraocular Pressure Algorithms Male Retrospective Studies Middle Aged Female Deep Learning Aged ROC Curve Cross-Sectional Studies Vision Disorders
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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.
Middle Aged Aged Humans Retinal Ganglion Cells Follow-Up Studies Visual Fields Glaucoma Aged, 80 and over Tomography, Optical Coherence Forecasting Young Adult Nerve Fibers Retrospective Studies Adult Optic Disk Female Intraocular Pressure Male
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