AIMC Topic: Visual Fields

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An Artificial Intelligence Approach to Detect Visual Field Progression in Glaucoma Based on Spatial Pattern Analysis.

Investigative ophthalmology & visual science Jan 2, 2019
PURPOSE: To detect visual field (VF) progression by analyzing spatial pattern changes.
Cohort Studies Follow-Up Studies Vision Disorders Disease Progression Diagnosis, Computer-Assisted Visual Field Tests False Positive Reactions Spatial Processing Predictive Value of Tests Visual Fields Glaucoma Artificial Intelligence Humans
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Deep-learning Classifier With an Ultrawide-field Scanning Laser Ophthalmoscope Detects Glaucoma Visual Field Severity.

Journal of glaucoma Jul 1, 2018
PURPOSE: To evaluate the accuracy of detecting glaucoma visual field defect severity using deep-learning (DL) classifier with an ultrawide-field scanning laser ophthalmoscope.
Sensitivity and Specificity Cross-Sectional Studies Aged Adult Middle Aged Severity of Illness Index Deep Learning Intraocular Pressure Male Humans Glaucoma Female Reproducibility of Results Retrospective Studies Image Interpretation, Computer-Assisted Microscopy, Confocal Visual Fields Vision Disorders Visual Field Tests Ophthalmoscopes
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Deep Learning for Predicting Refractive Error From Retinal Fundus Images.

Investigative ophthalmology & visual science Jun 1, 2018
PURPOSE: We evaluate how deep learning can be applied to extract novel information such as refractive error from retinal fundus imaging.
Visual Fields Vision Tests Retina Datasets as Topic Humans Algorithms Adult Deep Learning Middle Aged Refractive Errors Female Male Fundus Oculi Refraction, Ocular Aged
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A Deep Learning Approach to Digitally Stain Optical Coherence Tomography Images of the Optic Nerve Head.

Investigative ophthalmology & visual science Jan 1, 2018
PURPOSE: To develop a deep learning approach to digitally stain optical coherence tomography (OCT) images of the optic nerve head (ONH).
Optic Disk Retinal Ganglion Cells Tomography, Optical Coherence Visual Fields Humans Nerve Fibers Glaucoma Female Male Machine Learning Middle Aged Reproducibility of Results Algorithms Adult
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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...
Glaucoma, Open-Angle Intraocular Pressure Ocular Hypertension Humans Tomography, Optical Coherence Retinal Ganglion Cells Nerve Fibers Machine Learning Neural Networks, Computer Visual Fields Reproducibility of Results
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