AIMC Topic: Geographic Atrophy

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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 ...
Humans Prognosis Female Aged Aged, 80 and over Early Medical Intervention Geographic Atrophy Tomography, Optical Coherence Macular Degeneration Early Diagnosis Wet Macular Degeneration Male Disease Progression Deep Learning Imaging, Three-Dimensional
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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.
Male Disease Progression Algorithms Visual Acuity Prospective Studies Tomography, Optical Coherence Female Aged Fundus Oculi Humans Fluorescein Angiography Artificial Intelligence Retinal Cone Photoreceptor Cells Retinal Rod Photoreceptor Cells 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 ...
Aged Aged, 80 and over Male Disease Progression Deep Learning Humans Algorithms Prospective Studies Female Tomography, Optical Coherence Macular Degeneration Retinal Pigment Epithelium Geographic Atrophy
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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.
Female Fluorescein Angiography Aged Geographic Atrophy Male Severity of Illness Index Humans Disease Progression Deep Learning Forecasting Follow-Up Studies Prospective Studies Fundus Oculi Retina
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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).
Humans Female Reproducibility of Results Aged Aged, 80 and over Sensitivity and Specificity Male Middle Aged Deep Learning Physical Examination Geographic Atrophy Image Processing, Computer-Assisted Area Under Curve Diagnostic Techniques, Ophthalmological Photography
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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...
Tomography, Optical Coherence Retina Image Interpretation, Computer-Assisted Geographic Atrophy Humans Machine Learning
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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...
Retinal Drusen Geographic Atrophy Risk Factors Humans Aged Deep Learning Middle Aged Prospective Studies Aged, 80 and over Models, Theoretical Female Disease Progression Sensitivity and Specificity Area Under Curve Reproducibility of Results Severity of Illness Index Male Diagnostic Techniques, Ophthalmological Diagnosis, Computer-Assisted Photography
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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.
Male Middle Aged Disease Progression Diagnosis, Computer-Assisted Humans Tomography, Optical Coherence Female Neural Networks, Computer Fundus Oculi Reproducibility of Results Machine Learning Healthy Volunteers Aged Algorithms Fluorescein Angiography Sensitivity and Specificity Geographic Atrophy
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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 ...
Tomography, Optical Coherence Male Visual Fields Middle Aged Visual Field Tests Humans Deep Learning Visual Acuity Retinal Pigment Epithelium Prospective Studies Geographic Atrophy Female Aged Aged, 80 and over
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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 ...
Imaging, Three-Dimensional Tomography, Optical Coherence Macular Degeneration Geographic Atrophy Humans Neural Networks, Computer Female Aged Male Disease Progression Deep Learning
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