AIMC Topic: Visual Field Tests

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Detecting Glaucoma in the Ocular Hypertension Study Using Deep Learning.

JAMA ophthalmology Apr 1, 2022
IMPORTANCE: Automated deep learning (DL) analyses of fundus photographs potentially can reduce the cost and improve the efficiency of reading center assessment of end points in clinical trials.
Humans Deep Learning Visual Field Tests Optic Nerve Diseases Glaucoma, Open-Angle Ocular Hypertension Glaucoma Female Intraocular Pressure Male Middle Aged
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Identifying the Retinal Layers Linked to Human Contrast Sensitivity Via Deep Learning.

Investigative ophthalmology & visual science Feb 1, 2022
PURPOSE: Luminance contrast is the fundamental building block of human spatial vision. Therefore contrast sensitivity, the reciprocal of contrast threshold required for target detection, has been a barometer of human visual function. Although retinal...
Visual Field Tests Contrast Sensitivity Glaucoma, Open-Angle Retinal Neurons Visual Acuity Tomography, Optical Coherence Macular Degeneration Visual Fields Humans Neural Networks, Computer Female Aged Aged, 80 and over Young Adult Adult Male Middle Aged Deep Learning
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Individualized Glaucoma Change Detection Using Deep Learning Auto Encoder-Based Regions of Interest.

Translational vision science & technology Jul 1, 2021
PURPOSE: To compare change over time in eye-specific optical coherence tomography (OCT) retinal nerve fiber layer (RNFL)-based region-of-interest (ROI) maps developed using unsupervised deep-learning auto-encoders (DL-AE) to circumpapillary RNFL (cpR...
Humans Glaucoma Retinal Ganglion Cells Intraocular Pressure Disease Progression Visual Fields Deep Learning Visual Field Tests Nerve Fibers Optic Nerve Diseases Glaucoma, Open-Angle
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Assessment of a Segmentation-Free Deep Learning Algorithm for Diagnosing Glaucoma From Optical Coherence Tomography Scans.

JAMA ophthalmology Apr 1, 2020
IMPORTANCE: Conventional segmentation of the retinal nerve fiber layer (RNFL) is prone to errors that may affect the accuracy of spectral-domain optical coherence tomography (SD-OCT) scans in detecting glaucomatous damage.
Intraocular Pressure Male Middle Aged Deep Learning ROC Curve Aged Aged, 80 and over Adult Cross-Sectional Studies Glaucoma, Open-Angle Gonioscopy Slit Lamp Microscopy Tomography, Optical Coherence Nerve Fibers Retinal Ganglion Cells Visual Fields Visual Field Tests Humans Algorithms Female
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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.
Diagnosis, Computer-Assisted Cohort Studies Visual Fields Visual Field Tests Glaucoma, Open-Angle Ocular Hypertension Tonometry, Ocular Reproducibility of Results Aged Intraocular Pressure Male Middle Aged Machine Learning Algorithms Forecasting Female Humans
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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 Visual Field Tests Machine Learning Antigens, Neoplasm Cytoskeletal Proteins Female Retinal Cone Photoreceptor Cells Adult Neoplasm Proteins Genetic Therapy Calmodulin-Binding Proteins Young Adult Male Tomography, Optical Coherence Cell Cycle Proteins Leber Congenital Amaurosis Mutation Color Vision Defects Visual Fields Retinitis Pigmentosa Middle Aged Adolescent
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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.
Vision Disorders Predictive Value of Tests Visual Fields Visual Field Tests False Positive Reactions Artificial Intelligence Spatial Processing Disease Progression Diagnosis, Computer-Assisted Cohort Studies Follow-Up Studies 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 Aged Adult Deep Learning Sensitivity and Specificity Cross-Sectional Studies Microscopy, Confocal Humans Intraocular Pressure Vision Disorders Glaucoma Male Image Interpretation, Computer-Assisted Retrospective Studies Middle Aged Visual Fields Severity of Illness Index
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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 Unsupervised Machine Learning Principal Component Analysis Tomography, Optical Coherence Adult Optic Disk Nerve Fibers Intraocular Pressure Retinal Ganglion Cells Humans Male Visual Field Tests Retrospective Studies Middle Aged Optic Nerve Diseases Female Disease Progression Glaucoma, Open-Angle Aged
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