AIMC Topic: Wet Macular Degeneration
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Predicting Visual Acuity Responses to Anti-VEGF Treatment in the Comparison of Age-related Macular Degeneration Treatments Trials Using Machine Learning.
Ophthalmology. Retina
Nov 24, 2023
PURPOSE: To evaluate multiple machine learning (ML) models for predicting 2-year visual acuity (VA) responses to anti-vascular endothelial growth factor (anti-VEGF) treatment in the Comparison of Age-related Macular Degeneration (AMD) Treatments Tria...
Understanding and interpreting CNN's decision in optical coherence tomography-based AMD detection.
European journal of ophthalmology
Sep 6, 2023
INTRODUCTION: Automated assessment of age-related macular degeneration (AMD) using optical coherence tomography (OCT) has gained significant research attention in recent years. Though a list of convolutional neural network (CNN)-based methods has bee...
Deep-learning based automated quantification of critical optical coherence tomography features in neovascular age-related macular degeneration.
Eye (London, England)
Sep 5, 2023
PURPOSE: To validate a deep learning algorithm for automated intraretinal fluid (IRF), subretinal fluid (SRF) and neovascular pigment epithelium detachment (nPED) segmentations in neovascular age-related macular degeneration (nAMD).
Performance of retinal fluid monitoring in OCT imaging by automated deep learning versus human expert grading in neovascular AMD.
Eye (London, England)
Jun 13, 2023
PURPOSE: To evaluate the reliability of automated fluid detection in identifying retinal fluid activity in OCT scans of patients treated with anti-VEGF therapy for neovascular age-related macular degeneration by correlating human expert and automated...
Deep learning to detect macular atrophy in wet age-related macular degeneration using optical coherence tomography.
Scientific reports
May 22, 2023
Here, we have developed a deep learning method to fully automatically detect and quantify six main clinically relevant atrophic features associated with macular atrophy (MA) using optical coherence tomography (OCT) analysis of patients with wet age-r...
KFWC: A Knowledge-Driven Deep Learning Model for Fine-grained Classification of Wet-AMD.
Computer methods and programs in biomedicine
Dec 15, 2022
BACKGROUND AND OBJECTIVES: Automated diagnosis using deep neural networks can help ophthalmologists detect the blinding eye disease wet Age-related Macular Degeneration (AMD). Wet-AMD has two similar subtypes, Neovascular AMD and Polypoidal Choroidal...
Correlation of vascular and fluid-related parameters in neovascular age-related macular degeneration using deep learning.
Acta ophthalmologica
Aug 1, 2022
PURPOSE: To identify correlations between the vascular characteristics of macular neovascularization (MNV) obtained by optical coherence tomography angiography (OCTA) and distinct retinal fluid volumes in neovascular age-related macular degeneration ...
Therapeutic response in the HAWK and HARRIER trials using deep learning in retinal fluid volume and compartment analysis.
Eye (London, England)
May 6, 2022
OBJECTIVES: To assess the therapeutic response to brolucizumab and aflibercept by deep learning/OCT-based analysis of macular fluid volumes in neovascular age-related macular degeneration.
Artificial intelligence based detection of age-related macular degeneration using optical coherence tomography with unique image preprocessing.
European journal of ophthalmology
Apr 25, 2022
PURPOSE: The aim of the study is to improve the accuracy of age related macular degeneration (AMD) disease in its earlier phases with proposed Capsule Network (CapsNet) architecture trained on speckle noise reduced spectral domain optical coherence t...
Automated diagnosis of age-related macular degeneration using multi-modal vertical plane feature fusion via deep learning.
Medical physics
Mar 11, 2022
PURPOSE: To develop a computer-aided diagnostic (CADx) system of age-related macular degeneration (AMD) through feature fusion between infrared reflectance (IR) and optical coherence tomography (OCT) modalities in order to explore the superiority of ...
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