AIMC Topic: Ocular Hypertension

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Deep Learning Identifies High-Quality Fundus Photographs and Increases Accuracy in Automated Primary Open Angle Glaucoma Detection.

Translational vision science & technology Jan 2, 2024
PURPOSE: To develop and evaluate a deep learning (DL) model to assess fundus photograph quality, and quantitatively measure its impact on automated POAG detection in independent study populations.
Humans Glaucoma Diagnostic Techniques, Ophthalmological Glaucoma, Open-Angle Ocular Hypertension Deep Learning Fundus Oculi
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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.
Ocular Hypertension Optic Nerve Diseases Glaucoma, Open-Angle Intraocular Pressure Glaucoma Middle Aged Deep Learning Male Humans Female Visual Field Tests
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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.
Middle Aged Diagnosis, Computer-Assisted Intraocular Pressure Male Visual Field Tests Glaucoma, Open-Angle Ocular Hypertension Cohort Studies Visual Fields Algorithms Forecasting Reproducibility of Results Aged Female Tonometry, Ocular Humans 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...
Visual Fields Humans Glaucoma, Open-Angle Nerve Fibers Intraocular Pressure Ocular Hypertension Retinal Ganglion Cells Machine Learning Neural Networks, Computer Tomography, Optical Coherence Reproducibility of Results
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Conditional asymptotic inference for the kernel association test.

Bioinformatics (Oxford, England) Dec 1, 2017
MOTIVATION: The kernel association test (KAT) is popular in biological studies for its ability to combine weak effects potentially of opposite direction. Its P-value is typically assessed via its (unconditional) asymptotic distribution. However, such...
Genotype Ocular Hypertension Humans Machine Learning
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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 Diagnostic Techniques, Ophthalmological Glaucoma, Open-Angle Middle Aged Retinal Ganglion Cells Risk Factors Glaucoma Sensitivity and Specificity Multivariate Analysis Aged Adult Female Refractive Errors Cross-Sectional Studies Predictive Value of Tests Humans Visual Acuity Intraocular Pressure Tomography, Optical Coherence Neural Networks, Computer Diagnosis, Differential Male Nerve Fibers
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