AIMC Topic: Visual Fields
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Using Multi-Layer Perceptron Driven Diagnosis to Compare Biomarkers for Primary Open Angle Glaucoma.
Investigative ophthalmology & visual science
Sep 3, 2024
PURPOSE: To use neural network machine learning (ML) models to identify the most relevant ocular biomarkers for the diagnosis of primary open-angle glaucoma (POAG).
Long-Term Rate of Optic Disc Rim Loss in Glaucoma Patients Measured From Optic Disc Photographs With a Deep Neural Network.
Translational vision science & technology
Sep 3, 2024
PURPOSE: This study uses deep neural network-generated rim-to-disc area ratio (RADAR) measurements and the disc damage likelihood scale (DDLS) to measure the rate of optic disc rim loss in a large cohort of glaucoma patients.
Transformer-Based Deep Learning Prediction of 10-Degree Humphrey Visual Field Tests From 24-Degree Data.
Translational vision science & technology
Aug 1, 2024
PURPOSE: To predict 10-2 Humphrey visual fields (VFs) from 24-2 VFs and associated non-total deviation features using deep learning.
Visual Field Prognosis From Macula and Circumpapillary Spectral Domain Optical Coherence Tomography.
Translational vision science & technology
Jun 3, 2024
PURPOSE: To explore the structural-functional loss relationship from optic-nerve-head- and macula-centred spectral-domain (SD) Optical Coherence Tomography (OCT) images in the full spectrum of glaucoma patients using deep-learning methods.
Prediction of Central Visual Field Measures From Macular OCT Volume Scans With Deep Learning.
Translational vision science & technology
Nov 1, 2023
PURPOSE: Predict central 10° global and local visual field (VF) measurements from macular optical coherence tomography (OCT) volume scans with deep learning (DL).
Improving the Accuracy and Speed of Visual Field Testing in Glaucoma With Structural Information and Deep Learning.
Translational vision science & technology
Oct 3, 2023
PURPOSE: To assess the performance of a perimetric strategy using structure-function predictions from a deep learning (DL) model.
Segmentation-Free OCT-Volume-Based Deep Learning Model Improves Pointwise Visual Field Sensitivity Estimation.
Translational vision science & technology
Jun 1, 2023
PURPOSE: The structural changes measured by optical coherence tomography (OCT) are related to functional changes in visual fields (VFs). This study aims to accurately assess the structure-function relationship and overcome the challenges brought by t...
Medical Application of Geometric Deep Learning for the Diagnosis of Glaucoma.
Translational vision science & technology
Feb 1, 2023
PURPOSE: (1) To assess the performance of geometric deep learning in diagnosing glaucoma from a single optical coherence tomography (OCT) scan of the optic nerve head and (2) to compare its performance to that obtained with a three-dimensional (3D) c...
PyVisualFields: A Python Package for Visual Field Analysis.
Translational vision science & technology
Feb 1, 2023
PURPOSE: Artificial intelligence (AI) methods are changing all areas of research and have a variety of capabilities of analysis in ophthalmology, specifically in visual fields (VFs) to detect or predict vision loss progression. Whereas most of the AI...
Evaluation of Generative Adversarial Networks for High-Resolution Synthetic Image Generation of Circumpapillary Optical Coherence Tomography Images for Glaucoma.
JAMA ophthalmology
Oct 1, 2022
IMPORTANCE: Deep learning (DL) networks require large data sets for training, which can be challenging to collect clinically. Generative models could be used to generate large numbers of synthetic optical coherence tomography (OCT) images to train su...
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