AIMC Topic: Glaucoma, Open-Angle

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Predicting the Glaucomatous Central 10-Degree Visual Field From Optical Coherence Tomography Using Deep Learning and Tensor Regression.

American journal of ophthalmology May 6, 2020
PURPOSE: To predict the visual field (VF) of glaucoma patients within the central 10° from optical coherence tomography (OCT) measurements using deep learning and tensor regression.
Female Aged Adult Sensitivity and Specificity Cross-Sectional Studies Intraocular Pressure Humans Retrospective Studies Visual Fields Visual Field Tests Optic Nerve Diseases Glaucoma, Open-Angle Gonioscopy Slit Lamp Microscopy Axial Length, Eye Male Middle Aged Deep Learning Visual Acuity Vision Disorders 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.
Humans Optic Disk Female Intraocular Pressure Retinal Ganglion Cells Cross-Sectional Studies Visual Fields Male Glaucoma, Open-Angle Middle Aged Deep Learning Structure-Activity Relationship Tomography, Optical Coherence Nerve Fibers
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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.
Humans Glaucoma, Angle-Closure Tonometry, Ocular Low Tension Glaucoma Male Machine Learning Middle Aged Female Pilot Projects Reproducibility of Results Smartphone Aged Glaucoma, Open-Angle Adult Ocular Hypertension Intraocular Pressure
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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.
Intraocular Pressure Male Middle Aged Deep Learning ROC Curve Vision Disorders Humans Algorithms Retrospective Studies Female Aged Cross-Sectional Studies Visual Field Tests Optic Nerve Diseases Photography Physical Examination Glaucoma, Open-Angle Gonioscopy Area Under Curve Tomography, Optical Coherence Fundus Oculi Nerve Fibers Retinal Ganglion Cells Visual Fields
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Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs.

Ophthalmology Sep 24, 2019
PURPOSE: To develop and validate a deep learning (DL) algorithm that predicts referable glaucomatous optic neuropathy (GON) and optic nerve head (ONH) features from color fundus images, to determine the relative importance of these features in referr...
Middle Aged Deep Learning ROC Curve Area Under Curve Referral and Consultation Datasets as Topic Nerve Fibers Optic Disk Retinal Ganglion Cells Optic Nerve Diseases Ophthalmologists Glaucoma, Open-Angle Specialization Humans Retrospective Studies Female Aged Sensitivity and Specificity Male
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Machine Learning-Based Predictive Modeling of Surgical Intervention in Glaucoma Using Systemic Data From Electronic Health Records.

American journal of ophthalmology Jul 16, 2019
PURPOSE: To predict the need for surgical intervention in patients with primary open-angle glaucoma (POAG) using systemic data in electronic health records (EHRs).
Humans Machine Learning Retrospective Studies Electronic Health Records Female Reproducibility of Results Disease Progression Aged ROC Curve Models, Statistical Aged, 80 and over Odds Ratio Sensitivity and Specificity Glaucoma, Open-Angle Intraocular Pressure Filtering Surgery Male Middle Aged
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Evaluation of an AI system for the automated detection of glaucoma from stereoscopic optic disc photographs: the European Optic Disc Assessment Study.

Eye (London, England) Jul 2, 2019
OBJECTIVES: To evaluate the performance of a deep learning based Artificial Intelligence (AI) software for detection of glaucoma from stereoscopic optic disc photographs, and to compare this performance to the performance of a large cohort of ophthal...
Optic Nerve Diseases False Positive Reactions Photography Ophthalmologists Glaucoma, Open-Angle Optometrists Humans Artificial Intelligence Retrospective Studies Reproducibility of Results Sensitivity and Specificity Diagnosis, Computer-Assisted Deep Learning Clinical Competence ROC Curve Europe Predictive Value of Tests Observer Variation Optic Disk
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A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs.

American journal of ophthalmology Jan 26, 2019
PURPOSE: To train a deep learning (DL) algorithm that quantifies glaucomatous neuroretinal damage on fundus photographs using the minimum rim width relative to Bruch membrane opening (BMO-MRW) from spectral-domain optical coherence tomography (SDOCT)...
Retinal Ganglion Cells Visual Fields Visual Field Tests Optic Nerve Diseases Photography Glaucoma, Open-Angle Bruch Membrane Humans Deep Learning Algorithms Vision Disorders Retrospective Studies Area Under Curve Female Tomography, Optical Coherence Aged Nerve Fibers Aged, 80 and over Optic Disk Cross-Sectional Studies Intraocular Pressure Male Middle Aged
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From Machine to Machine: An OCT-Trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs.

Ophthalmology Dec 20, 2018
PURPOSE: Previous approaches using deep learning (DL) algorithms to classify glaucomatous damage on fundus photographs have been limited by the requirement for human labeling of a reference training set. We propose a new approach using quantitative s...
Deep Learning Aged Humans Optic Disk ROC Curve Algorithms Aged, 80 and over Glaucoma, Open-Angle Retinal Ganglion Cells Visual Acuity Female Cross-Sectional Studies Tonometry, Ocular Tomography, Optical Coherence Visual Fields Intraocular Pressure Gonioscopy Fundus Oculi Visual Field Tests Male Nerve Fibers Optic Nerve Diseases Middle Aged Photography
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Using Deep Learning and Transfer Learning to Accurately Diagnose Early-Onset Glaucoma From Macular Optical Coherence Tomography Images.

American journal of ophthalmology Oct 12, 2018
PURPOSE: We sought to construct and evaluate a deep learning (DL) model to diagnose early glaucoma from spectral-domain optical coherence tomography (OCT) images.
Area Under Curve Visual Field Tests Tomography, Optical Coherence Humans Sensitivity and Specificity Transfer, Psychology Glaucoma Datasets as Topic Intraocular Pressure Glaucoma, Open-Angle Female Nerve Fibers Male Gonioscopy Reproducibility of Results Retinal Ganglion Cells Middle Aged Aged Visual Fields Deep Learning Adult ROC Curve
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