AIMC Topic: Glaucoma, Open-Angle

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Screening Glaucoma With Red-free Fundus Photography Using Deep Learning Classifier and Polar Transformation.

Journal of glaucoma Mar 1, 2019
UNLABELLED: PRéCIS:: The novel proposed algorithm using deep learning classifier and polar transformation technique can be an economical as well as an effective tool for early detection of glaucomatous RNFL defect.
Area Under Curve Tomography, Optical Coherence Image Interpretation, Computer-Assisted Middle Aged Deep Learning ROC Curve Diagnostic Techniques, Ophthalmological Photography Glaucoma, Open-Angle Nerve Fibers Retinal Ganglion Cells Ophthalmologic Surgical Procedures Humans Retrospective Studies Female Adult Intraocular Pressure Male
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Macular Vessel Density and Ganglion Cell/Inner Plexiform Layer Thickness and Their Combinational Index Using Artificial Intelligence.

Journal of glaucoma Sep 1, 2018
PURPOSE: To evaluate the relationship between macular vessel density and ganglion cell to inner plexiform layer thickness (GCIPLT) and to compare their diagnostic performance. We attempted to develop a new combined parameter using an artificial neura...
Aged Adult Cross-Sectional Studies Artificial Intelligence Female Case-Control Studies ROC Curve Area Under Curve Intraocular Pressure Male Middle Aged Humans Neural Networks, Computer Retinal Ganglion Cells Retinal Vessels Glaucoma, Open-Angle Tomography, Optical Coherence Nerve Fibers
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Retinal Nerve Fiber Layer Features Identified by Unsupervised Machine Learning on Optical Coherence Tomography Scans Predict Glaucoma Progression.

Investigative ophthalmology & visual science Jun 1, 2018
PURPOSE: To apply computational techniques to wide-angle swept-source optical coherence tomography (SS-OCT) images to identify novel, glaucoma-related structural features and improve detection of glaucoma and prediction of future glaucomatous progres...
Visual Field Tests Optic Nerve Diseases Glaucoma, Open-Angle Tonometry, Ocular Nerve Fibers Optic Disk Retinal Ganglion Cells Principal Component Analysis Tomography, Optical Coherence Adult Retrospective Studies Middle Aged Intraocular Pressure Humans Female Male Disease Progression Aged Unsupervised Machine Learning
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Hybrid Deep Learning on Single Wide-field Optical Coherence tomography Scans Accurately Classifies Glaucoma Suspects.

Journal of glaucoma Dec 1, 2017
PURPOSE: Existing summary statistics based upon optical coherence tomographic (OCT) scans and/or visual fields (VFs) are suboptimal for distinguishing between healthy and glaucomatous eyes in the clinic. This study evaluates the extent to which a hyb...
Intraocular Pressure Ocular Hypertension Neural Networks, Computer Tomography, Optical Coherence Humans Retinal Ganglion Cells Nerve Fibers Visual Fields Machine Learning Glaucoma, Open-Angle Reproducibility of Results
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Artificial Neural Network Approach for Differentiating Open-Angle Glaucoma From Glaucoma Suspect Without a Visual Field Test.

Investigative ophthalmology & visual science Jun 1, 2015
PURPOSE: To increase the effectiveness of treating open-angle glaucoma (OAG), we tried to find a screening method of differentiating OAG from glaucoma suspect (GS) without a visual field (VF) test.
Ocular Hypertension Visual Acuity Aged Diagnosis, Differential Adult Glaucoma Predictive Value of Tests Sensitivity and Specificity Humans Cross-Sectional Studies Female Middle Aged Neural Networks, Computer Intraocular Pressure Risk Factors Refractive Errors Male Multivariate Analysis Diagnostic Techniques, Ophthalmological Tomography, Optical Coherence Glaucoma, Open-Angle Nerve Fibers Retinal Ganglion Cells
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