AIMC Topic: Geographic Atrophy
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Deep Learning to Predict Geographic Atrophy Area and Growth Rate from Multimodal Imaging.
Ophthalmology. Retina
Aug 28, 2022
OBJECTIVE: To develop deep learning models for annualized geographic atrophy (GA) growth rate prediction using fundus autofluorescence (FAF) images and spectral-domain OCT volumes from baseline visits, which can be used for prognostic covariate adjus...
A Deep Learning Model for Automated Segmentation of Geographic Atrophy Imaged Using Swept-Source OCT.
Ophthalmology. Retina
Aug 12, 2022
PURPOSE: To present a deep learning algorithm for segmentation of geographic atrophy (GA) using en face swept-source OCT (SS-OCT) images that is accurate and reproducible for the assessment of GA growth over time.
Predicting Topographic Disease Progression and Treatment Response of Pegcetacoplan in Geographic Atrophy Quantified by Deep Learning.
Ophthalmology. Retina
Aug 7, 2022
PURPOSE: To identify disease activity and effects of intravitreal pegcetacoplan treatment on the topographic progression of geographic atrophy (GA) secondary to age-related macular degeneration quantified in spectral-domain OCT (SD-OCT) by automated ...
Clinically relevant deep learning for detection and quantification of geographic atrophy from optical coherence tomography: a model development and external validation study.
The Lancet. Digital health
Sep 8, 2021
BACKGROUND: Geographic atrophy is a major vision-threatening manifestation of age-related macular degeneration, one of the leading causes of blindness globally. Geographic atrophy has no proven treatment or method for easy detection. Rapid, reliable,...
Deep learning in geographic atrophy: the best is yet to come.
The Lancet. Digital health
Sep 8, 2021
Quantification of Key Retinal Features in Early and Late Age-Related Macular Degeneration Using Deep Learning.
American journal of ophthalmology
Jan 8, 2021
PURPOSE: We sought to develop and validate a deep learning model for segmentation of 13 features associated with neovascular and atrophic age-related macular degeneration (AMD).
Geographic Atrophy
Intravitreal Injections
Recombinant Fusion Proteins
Ranibizumab
Retinal Drusen
Choroidal Neovascularization
Wet Macular Degeneration
Receptors, Vascular Endothelial Growth Factor
Visual Acuity
Tomography, Optical Coherence
Vascular Endothelial Growth Factor A
ROC Curve
Angiogenesis Inhibitors
Models, Statistical
Female
Aged
Aged, 80 and over
Middle Aged
Deep Learning
Sensitivity and Specificity
Male
Humans
Neural Networks, Computer
Deep learning-based classification of retinal atrophy using fundus autofluorescence imaging.
Computers in biology and medicine
Dec 28, 2020
PURPOSE: To automatically classify retinal atrophy according to its etiology, using fundus autofluorescence (FAF) images, using a deep learning model.
MS-CAM: Multi-Scale Class Activation Maps for Weakly-Supervised Segmentation of Geographic Atrophy Lesions in SD-OCT Images.
IEEE journal of biomedical and health informatics
Dec 4, 2020
As one of the most critical characteristics in advanced stage of non-exudative Age-related Macular Degeneration (AMD), Geographic Atrophy (GA) is one of the significant causes of sustained visual acuity loss. Automatic localization of retinal regions...
Predicting conversion to wet age-related macular degeneration using deep learning.
Nature medicine
May 18, 2020
Progression to exudative 'wet' age-related macular degeneration (exAMD) is a major cause of visual deterioration. In patients diagnosed with exAMD in one eye, we introduce an artificial intelligence (AI) system to predict progression to exAMD in the ...
Determinants of Cone and Rod Functions in Geographic Atrophy: AI-Based Structure-Function Correlation.
American journal of ophthalmology
Apr 11, 2020
PURPOSE: To investigate the association between retinal microstructure and cone and rod function in geographic atrophy (GA) secondary to age-related macular degeneration (AMD) by using artificial intelligence (AI) algorithms.
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