AIMC Topic: Geographic Atrophy
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Role of Deep Learning-Quantified Hyperreflective Foci for the Prediction of Geographic Atrophy Progression.
American journal of ophthalmology
Apr 8, 2020
PURPOSE: To quantitatively measure hyperreflective foci (HRF) during the progression of geographic atrophy (GA) secondary to age-related macular degeneration (AMD) using deep learning (DL) and investigate the association with local and global growth ...
A Deep Learning Model for Segmentation of Geographic Atrophy to Study Its Long-Term Natural History.
Ophthalmology
Feb 15, 2020
PURPOSE: To develop and validate a deep learning model for the automatic segmentation of geographic atrophy (GA) using color fundus images (CFIs) and its application to study the growth rate of GA.
A Deep Learning Approach for Automated Detection of Geographic Atrophy from Color Fundus Photographs.
Ophthalmology
Jun 11, 2019
PURPOSE: To assess the utility of deep learning in the detection of geographic atrophy (GA) from color fundus photographs and to explore potential utility in detecting central GA (CGA).
Automated geographic atrophy segmentation for SD-OCT images based on two-stage learning model.
Computers in biology and medicine
Dec 28, 2018
Automatic and reliable segmentation for geographic atrophy in spectral-domain optical coherence tomography (SD-OCT) images is a challenging task. To develop an effective segmentation method, a two-stage deep learning framework based on an auto-encode...
DeepSeeNet: A Deep Learning Model for Automated Classification of Patient-based Age-related Macular Degeneration Severity from Color Fundus Photographs.
Ophthalmology
Nov 22, 2018
PURPOSE: In assessing the severity of age-related macular degeneration (AMD), the Age-Related Eye Disease Study (AREDS) Simplified Severity Scale predicts the risk of progression to late AMD. However, its manual use requires the time-consuming partic...
Diagnostic Techniques, Ophthalmological
Photography
Reproducibility of Results
Retinal Drusen
Geographic Atrophy
Risk Factors
Humans
Severity of Illness Index
Aged
Sensitivity and Specificity
Prospective Studies
Deep Learning
Aged, 80 and over
Diagnosis, Computer-Assisted
Male
Female
Models, Theoretical
Middle Aged
Area Under Curve
Disease Progression
Deep learning-based detection and classification of geographic atrophy using a deep convolutional neural network classifier.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Aug 8, 2018
PURPOSE: To automatically detect and classify geographic atrophy (GA) in fundus autofluorescence (FAF) images using a deep learning algorithm.
Structure-Function Correlation of Deep-Learning Quantified Ellipsoid Zone and Retinal Pigment Epithelium Loss and Microperimetry in Geographic Atrophy.
Investigative ophthalmology & visual science
Mar 3, 2025
PURPOSE: The purpose of this study was to define structure-function correlation of geographic atrophy (GA) on optical coherence tomography (OCT) and functional testing on microperimetry (MP) based on deep-learning (DL)-quantified spectral-domain OCT ...
Deep Learning Approaches to Predict Geographic Atrophy Progression Using Three-Dimensional OCT Imaging.
Translational vision science & technology
Feb 3, 2025
PURPOSE: To evaluate the performance of various approaches of processing three-dimensional (3D) optical coherence tomography (OCT) images for deep learning models in predicting area and future growth rate of geographic atrophy (GA) lesions caused by ...
Topographic Clinical Insights From Deep Learning-Based Geographic Atrophy Progression Prediction.
Translational vision science & technology
Aug 1, 2024
PURPOSE: To explore the contributions of fundus autofluorescence (FAF) topographic imaging features to the performance of convolutional neural network-based deep learning (DL) algorithms in predicting geographic atrophy (GA) growth rate.
Quantifying Geographic Atrophy in Age-Related Macular Degeneration: A Comparative Analysis Across 12 Deep Learning Models.
Investigative ophthalmology & visual science
Jul 1, 2024
PURPOSE: AI algorithms have shown impressive performance in segmenting geographic atrophy (GA) from fundus autofluorescence (FAF) images. However, selection of artificial intelligence (AI) architecture is an important variable in model development. H...
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