AIMC Topic: Visual Fields

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Characterization of Central Visual Field Loss in End-stage Glaucoma by Unsupervised Artificial Intelligence.

JAMA ophthalmology Feb 1, 2020
IMPORTANCE: Although the central visual field (VF) in end-stage glaucoma may substantially vary among patients, structure-function studies and quality-of-life assessments are impeded by the lack of appropriate characterization of end-stage VF loss.
Humans Aged, 80 and over Cross-Sectional Studies Middle Aged Longitudinal Studies Visual Fields Glaucoma Artificial Intelligence Retrospective Studies Aged
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Accuracy of Kalman Filtering in Forecasting Visual Field and Intraocular Pressure Trajectory in Patients With Ocular Hypertension.

JAMA ophthalmology Dec 1, 2019
IMPORTANCE: Techniques that properly identify patients in whom ocular hypertension (OHTN) is likely to progress to open-angle glaucoma can assist clinicians with deciding on the frequency of monitoring and the potential benefit of early treatment.
Visual Field Tests Glaucoma, Open-Angle Ocular Hypertension Tonometry, Ocular Middle Aged Diagnosis, Computer-Assisted Cohort Studies Visual Fields Humans Machine Learning Algorithms Forecasting Aged Intraocular Pressure Male Female Reproducibility of Results
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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...
Machine Learning Female Young Adult Adult Humans Neoplasm Proteins Tomography, Optical Coherence Mutation Visual Fields Visual Field Tests Male Middle Aged Adolescent Antigens, Neoplasm Retinal Cone Photoreceptor Cells Cytoskeletal Proteins Calmodulin-Binding Proteins Genetic Therapy Leber Congenital Amaurosis Cell Cycle Proteins Retinitis Pigmentosa Color Vision Defects
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Neural population control via deep image synthesis.

Science (New York, N.Y.) May 3, 2019
Particular deep artificial neural networks (ANNs) are today's most accurate models of the primate brain's ventral visual stream. Using an ANN-driven image synthesis method, we found that luminous power patterns (i.e., images) can be applied to primat...
Nerve Net Visual Cortex Macaca Models, Neurological Neurons Visual Fields Neural Networks, Computer Animals
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Artificial intelligence in glaucoma.

Current opinion in ophthalmology Mar 1, 2019
PURPOSE OF REVIEW: The use of computers has become increasingly relevant to medical decision-making, and artificial intelligence methods have recently demonstrated significant advances in medicine. We therefore provide an overview of current artifici...
Diagnosis, Computer-Assisted Tomography, Optical Coherence Visual Fields Diagnostic Techniques, Ophthalmological Humans Glaucoma Artificial Intelligence
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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.
Spatial Processing Humans Predictive Value of Tests Glaucoma Visual Fields Artificial Intelligence Visual Field Tests False Positive Reactions Cohort Studies Disease Progression Follow-Up Studies Vision Disorders Diagnosis, Computer-Assisted
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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.
Retrospective Studies Female Reproducibility of Results Aged Cross-Sectional Studies Intraocular Pressure Male Adult Sensitivity and Specificity Humans Glaucoma Vision Disorders Image Interpretation, Computer-Assisted Visual Fields Middle Aged Ophthalmoscopes Visual Field Tests Severity of Illness Index Deep Learning Microscopy, Confocal
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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.
Adult Visual Fields Deep Learning Male Vision Tests Fundus Oculi Middle Aged Retina Humans Refraction, Ocular Datasets as Topic Algorithms Refractive Errors Female 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).
Nerve Fibers Optic Disk Retinal Ganglion Cells Visual Fields Adult Male Middle Aged Tomography, Optical Coherence Humans Glaucoma Algorithms Machine Learning Female Reproducibility of Results
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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...
Visual Fields Glaucoma, Open-Angle Ocular Hypertension Intraocular Pressure Tomography, Optical Coherence Humans Nerve Fibers Neural Networks, Computer Retinal Ganglion Cells Machine Learning Reproducibility of Results
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