AIMC Topic: Intraocular Pressure

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Efficacy for Differentiating Nonglaucomatous Versus Glaucomatous Optic Neuropathy Using Deep Learning Systems.

American journal of ophthalmology Apr 2, 2020
PURPOSE: We sought to assess the performance of deep learning approaches for differentiating nonglaucomatous optic neuropathy with disc pallor (NGON) vs glaucomatous optic neuropathy (GON) on color fundus photographs by the use of image recognition.
Humans Neural Networks, Computer Glaucoma Algorithms ROC Curve Area Under Curve Optic Disk Visual Field Tests Optic Nerve Diseases Photography Female Reproducibility of Results Sensitivity and Specificity Intraocular Pressure Male Deep Learning
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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 Glaucoma Retrospective Studies Deep Learning ROC Curve Follow-Up Studies Tomography, Optical Coherence Nerve Fibers Optic Disk Female Aged Intraocular Pressure Male Middle Aged Retinal Ganglion Cells Visual Fields Optic Nerve Diseases
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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...
Diagnostic Techniques, Ophthalmological Refractometry Adult Optic Nerve Sensitivity and Specificity Tomography, Optical Coherence Intraocular Pressure Algorithms Fundus Oculi Male Retrospective Studies Translational Research, Biomedical Middle Aged Case-Control Studies Retinal Ganglion Cells Humans Female Diagnosis, Computer-Assisted Glaucoma Aged Deep Learning Aged, 80 and over ROC Curve Young Adult Support Vector Machine Regression Analysis
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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, ...
Retinal Ganglion Cells Optic Nerve Diseases Diagnostic Techniques, Ophthalmological Intraocular Pressure Deep Learning ROC Curve Tomography, Optical Coherence Optic Disk Humans Neural Networks, Computer Glaucoma Retrospective Studies
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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.
Structure-Activity Relationship Tomography, Optical Coherence Nerve Fibers Optic Disk Female Visual Fields Retinal Ganglion Cells Cross-Sectional Studies Glaucoma, Open-Angle Intraocular Pressure Male Middle Aged Deep Learning Humans
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Development and validation of a machine learning, smartphone-based tonometer.

The British journal of ophthalmology Dec 23, 2019
BACKGROUND/AIMS: To compare intraocular pressure (IOP) measurements using a prototype smartphone tonometer with other tonometers used in clinical practice.
Glaucoma, Open-Angle Ocular Hypertension Glaucoma, Angle-Closure Tonometry, Ocular Low Tension Glaucoma Intraocular Pressure Humans Male Machine Learning Middle Aged Female Pilot Projects Reproducibility of Results Smartphone Aged Adult
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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 Female Young Adult Adult Cross-Sectional Studies Intraocular Pressure Male Disease Progression Myopia Diagnosis, Computer-Assisted ROC Curve Humans Visual Field Tests Gonioscopy Slit Lamp Microscopy Low Tension Glaucoma Area Under Curve Tomography, Optical Coherence Nerve Fibers Retinal Ganglion Cells Visual Fields
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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.
Aged Bayes Theorem Cross-Sectional Studies Visual Fields Humans Intraocular Pressure Visual Field Tests Glaucoma Male Artificial Intelligence Middle Aged Retrospective Studies Vision Disorders Female
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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...
Nerve Fibers Optic Disk Retinal Ganglion Cells Visual Fields Intraocular Pressure Humans Prospective Studies Male Glaucoma Female Middle Aged Aged Deep Learning Aged, 80 and over Adolescent Young Adult Tomography, Optical Coherence Adult
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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.
Tomography, Optical Coherence Humans Fundus Oculi Middle Aged Algorithms Optic Nerve Diseases Nerve Fibers Deep Learning Retrospective Studies Photography Retinal Ganglion Cells ROC Curve Female Physical Examination Visual Fields Vision Disorders Aged Glaucoma, Open-Angle Visual Field Tests Area Under Curve Cross-Sectional Studies Gonioscopy Intraocular Pressure Male
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