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Visual Field Tests

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Treatment Potential for Macular Cone Vision in Leber Congenital Amaurosis Due to CEP290 or NPHP5 Mutations: Predictions From Artificial Intelligence.

Investigative ophthalmology & visual science Jun 3, 2019
PURPOSE: To use supervised machine learning to predict visual function from retinal structure in retinitis pigmentosa (RP) and apply these estimates to CEP290- and NPHP5-associated Leber congenital amaurosis (LCA) to determine the potential for funct...
Humans Machine Learning Female Cytoskeletal Proteins Genetic Therapy Cell Cycle Proteins Visual Fields Visual Field Tests Calmodulin-Binding Proteins Leber Congenital Amaurosis Retinitis Pigmentosa Color Vision Defects Retinal Cone Photoreceptor Cells Adolescent Antigens, Neoplasm Tomography, Optical Coherence Mutation Neoplasm Proteins Young Adult Adult Male Middle Aged
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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.
Visual Fields Visual Field Tests Vision Disorders Predictive Value of Tests False Positive Reactions Spatial Processing Artificial Intelligence Disease Progression Cohort Studies Follow-Up Studies Diagnosis, Computer-Assisted Humans Glaucoma
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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.
Visual Field Tests Ophthalmoscopes Female Reproducibility of Results Glaucoma Adult Humans Deep Learning Male Sensitivity and Specificity Cross-Sectional Studies Middle Aged Microscopy, Confocal Image Interpretation, Computer-Assisted Retrospective Studies Vision Disorders Aged Intraocular Pressure Severity of Illness Index Visual Fields
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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...
Tonometry, Ocular Tomography, Optical Coherence Nerve Fibers Unsupervised Machine Learning Principal Component Analysis Visual Field Tests Optic Disk Optic Nerve Diseases Adult Female Humans Male Retinal Ganglion Cells Middle Aged Aged Glaucoma, Open-Angle Intraocular Pressure Disease Progression Retrospective Studies
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