AIMC Topic: Visual Fields

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Repeatability of Microperimetry in Areas of Retinal Pigment Epithelium and Photoreceptor Loss in Geographic Atrophy Supported by Artificial Intelligence-Based Optical Coherence Tomography Biomarker Quantification.

American journal of ophthalmology Nov 14, 2024
PURPOSE: Growing interest in microperimetry (MP) or fundus-controlled perimetry as a targeted psychometric testing method in geographic atrophy (GA) is warranted because of the disease subclinical/extrafoveal appearance or preexisting foveal loss wit...
Artificial Intelligence Prospective Studies Biomarkers Female Reproducibility of Results Humans Tomography, Optical Coherence Visual Fields Visual Field Tests Retinal Pigment Epithelium Photoreceptor Cells, Vertebrate Geographic Atrophy Aged Aged, 80 and over Male Middle Aged Visual Acuity
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Diagnostic Performance of the Offline Medios Artificial Intelligence for Glaucoma Detection in a Rural Tele-Ophthalmology Setting.

Ophthalmology. Glaucoma Sep 12, 2024
PURPOSE: This study assesses the diagnostic efficacy of offline Medios Artificial Intelligence (AI) glaucoma software in a primary eye care setting, using nonmydriatic fundus images from Remidio's Fundus-on-Phone (FOP NM-10). Artificial intelligence ...
Visual Acuity Ophthalmology Telemedicine Visual Fields Humans Glaucoma Artificial Intelligence Prospective Studies Female Aged Cross-Sectional Studies Intraocular Pressure Male Middle Aged Rural Population
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Identifying Factors Associated With Fast Visual Field Progression in Patients With Ocular Hypertension Based on Unsupervised Machine Learning.

Journal of glaucoma Aug 5, 2024
PRCIS: We developed unsupervised machine learning models to identify different subtypes of patients with ocular hypertension in terms of visual field (VF) progression and discovered 4 subtypes with different trends of VF worsening. We then identified...
Humans Female Risk Factors Aged Cross-Sectional Studies Intraocular Pressure Vision Disorders Male Visual Fields Middle Aged Visual Field Tests Disease Progression Glaucoma, Open-Angle Follow-Up Studies Ocular Hypertension Unsupervised Machine Learning
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Prediction of visual field progression with serial optic disc photographs using deep learning.

The British journal of ophthalmology Jul 23, 2024
AIM: We tested the hypothesis that visual field (VF) progression can be predicted with a deep learning model based on longitudinal pairs of optic disc photographs (ODP) acquired at earlier time points during follow-up.
Disease Progression Deep Learning ROC Curve Follow-Up Studies Vision Disorders Female Aged Intraocular Pressure Male Middle Aged Optic Disk Visual Fields Visual Field Tests Optic Nerve Diseases Photography Humans Glaucoma Retrospective Studies
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Deep-learning-based prediction of glaucoma conversion in normotensive glaucoma suspects.

The British journal of ophthalmology Jun 20, 2024
BACKGROUND/AIMS: To assess the performance of deep-learning (DL) models for prediction of conversion to normal-tension glaucoma (NTG) in normotensive glaucoma suspect (GS) patients.
Blood Pressure Nerve Fibers Optic Disk Retinal Ganglion Cells Visual Fields Ocular Hypertension Tonometry, Ocular Low Tension Glaucoma Middle Aged Disease Progression Deep Learning ROC Curve Follow-Up Studies Retrospective Studies Female Aged Intraocular Pressure Humans Male Algorithms
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Estimating receptive fields of simple and complex cells in early visual cortex: A convolutional neural network model with parameterized rectification.

PLoS computational biology May 31, 2024
Neurons in the primary visual cortex respond selectively to simple features of visual stimuli, such as orientation and spatial frequency. Simple cells, which have phase-sensitive responses, can be modeled by a single receptive field filter in a linea...
Animals Computational Biology Models, Neurological Neurons Visual Fields Cats Photic Stimulation Visual Cortex Primary Visual Cortex Neural Networks, Computer
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Progression or Aging? A Deep Learning Approach for Distinguishing Glaucoma Progression From Age-Related Changes in OCT Scans.

American journal of ophthalmology May 3, 2024
PURPOSE: To develop deep learning (DL) algorithm to detect glaucoma progression using optical coherence tomography (OCT) images, in the absence of a reference standard.
Humans Neural Networks, Computer Deep Learning Follow-Up Studies Tomography, Optical Coherence Intraocular Pressure Glaucoma Nerve Fibers Retinal Ganglion Cells Male Optic Disk Aging Visual Fields Middle Aged Optic Nerve Diseases Disease Progression Glaucoma, Open-Angle Algorithms Retrospective Studies Female Aged
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Prediction of Visual Field Progression with Baseline and Longitudinal Structural Measurements Using Deep Learning.

American journal of ophthalmology Feb 12, 2024
PURPOSE: Identifying glaucoma patients at high risk of progression based on widely available structural data is an unmet task in clinical practice. We test the hypothesis that baseline or serial structural measures can predict visual field (VF) progr...
Follow-Up Studies Vision Disorders Area Under Curve Tomography, Optical Coherence Nerve Fibers Glaucoma Male Glaucoma, Open-Angle Optic Disk Humans Algorithms Middle Aged Retinal Ganglion Cells Retrospective Studies Disease Progression Visual Fields Female Deep Learning Visual Field Tests Aged ROC Curve Optic Nerve Diseases Intraocular Pressure
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A multi-label transformer-based deep learning approach to predict focal visual field progression.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie Feb 9, 2024
PURPOSE: Tracking functional changes in visual fields (VFs) through standard automated perimetry remains a clinical standard for glaucoma diagnosis. This study aims to develop and evaluate a deep learning (DL) model to predict regional VF progression...
Follow-Up Studies Optic Disk Visual Fields Humans Visual Field Tests Glaucoma Male Retrospective Studies Middle Aged Female Aged Disease Progression Deep Learning ROC Curve
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Opportunities for Improving Glaucoma Clinical Trials via Deep Learning-Based Identification of Patients with Low Visual Field Variability.

Ophthalmology. Glaucoma Jan 29, 2024
PURPOSE: Develop and evaluate the performance of a deep learning model (DLM) that forecasts eyes with low future visual field (VF) variability, and study the impact of using this DLM on sample size requirements for neuroprotective trials.
Tomography, Optical Coherence Visual Fields Visual Field Tests Clinical Trials as Topic Intraocular Pressure Humans Male Glaucoma Middle Aged Retrospective Studies Deep Learning Female Visual Acuity Aged
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