AIMC Topic: Visual Field Tests
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SMOTE-Enhanced Explainable Artificial Intelligence Model for Predicting Visual Field Progression in Myopic Normal Tension Glaucoma.
Journal of glaucoma
Apr 21, 2025
PRCIS: The AI model, enhanced by SMOTE to balance data classes, accurately predicted visual field deterioration in patients with myopic normal tension glaucoma. Using SHAP analysis, the key variables driving disease progression were identified.
Automated learning of glaucomatous visual fields from OCT images using a comprehensive, segmentation-free 3D convolutional neural network model.
Scientific reports
Apr 18, 2025
A segmentation-free 3D Convolutional Neural Network (3DCNN) model was adopted to estimate Visual Field (VF) in glaucoma cases using Optical Coherence Tomography (OCT) images. This study, conducted at a university hospital, included 6335 participants ...
PyGlaucoMetrics: A Stacked Weight-Based Machine Learning Approach for Glaucoma Detection Using Visual Field Data.
Medicina (Kaunas, Lithuania)
Mar 20, 2025
: Glaucoma (GL) classification is crucial for early diagnosis and treatment, yet relying solely on stand-alone models or International Classification of Diseases (ICD) codes is insufficient due to limited predictive power and inconsistencies in clini...
Predicting visual field global and local parameters from OCT measurements using explainable machine learning.
Scientific reports
Feb 16, 2025
Glaucoma is characterised by progressive vision loss due to retinal ganglion cell deterioration, leading to gradual visual field (VF) impairment. The standard VF test may be impractical in some cases, where optical coherence tomography (OCT) can offe...
Glaucoma detection and staging from visual field images using machine learning techniques.
PloS one
Jan 17, 2025
PURPOSE: In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (VFs) and classify glaucoma from early to the advanced stage to observe if the DL model can stage glau...
Validation of a Visual Field Prediction Tool for Glaucoma: A Multicenter Study Involving Patients With Glaucoma in the United Kingdom.
American journal of ophthalmology
Jan 13, 2025
PURPOSE: A previously developed machine-learning approach with Kalman filtering technology accurately predicted the disease trajectory for patients with various glaucoma types and severities using clinical trial data. This study assesses performance ...
Detection of glaucoma progression on longitudinal series of en-face macular optical coherence tomography angiography images with a deep learning model.
The British journal of ophthalmology
Nov 22, 2024
BACKGROUND/AIMS: To design a deep learning (DL) model for the detection of glaucoma progression with a longitudinal series of macular optical coherence tomography angiography (OCTA) images.
Fluorescein Angiography
Humans
Macula Lutea
Retrospective Studies
Tomography, Optical Coherence
Female
Disease Progression
Retinal Vessels
Optic Disk
Aged
Deep Learning
Glaucoma, Open-Angle
Retinal Ganglion Cells
ROC Curve
Intraocular Pressure
Visual Fields
Follow-Up Studies
Male
Visual Field Tests
Middle Aged
Topographic and quantitative correlation of structure and function using deep learning in subclinical biomarkers of intermediate age-related macular degeneration.
Scientific reports
Nov 15, 2024
To examine the morphological impact of deep learning (DL)-quantified biomarkers on point-wise sensitivity (PWS) using microperimetry (MP) and optical coherence tomography (OCT) in intermediate AMD (iAMD). Patients with iAMD were examined by OCT (Spec...
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...
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...
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