AIMC Topic: Macular Degeneration

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DeepAlienorNet: A deep learning model to extract clinical features from colour fundus photography in age-related macular degeneration.

Acta ophthalmologica
OBJECTIVE: This study aimed to develop a deep learning (DL) model, named 'DeepAlienorNet', to automatically extract clinical signs of age-related macular degeneration (AMD) from colour fundus photography (CFP).

Preliminary analysis of predicting the first recurrence in patients with neovascular age-related macular degeneration using deep learning.

BMC ophthalmology
BACKGROUND: To predict, using deep learning, the first recurrence in patients with neovascular age-related macular degeneration (nAMD) after three monthly loading injections of intravitreal anti-vascular endothelial growth factor (anti-VEGF).

Deep learning model for automatic differentiation of EMAP from AMD in macular atrophy.

Scientific reports
To create a deep learning (DL) classifier pre-trained on fundus autofluorescence (FAF) images that can assist the clinician in distinguishing age-related geographic atrophy from extensive macular atrophy and pseudodrusen-like appearance (EMAP). Patie...

Automated deep learning-based AMD detection and staging in real-world OCT datasets (PINNACLE study report 5).

Scientific reports
Real-world retinal optical coherence tomography (OCT) scans are available in abundance in primary and secondary eye care centres. They contain a wealth of information to be analyzed in retrospective studies. The associated electronic health records a...

Automatic detection and differential diagnosis of age-related macular degeneration from color fundus photographs using deep learning with hierarchical vision transformer.

Computers in biology and medicine
Age-related macular degeneration (AMD) is a leading cause of vision loss in the elderly, highlighting the need for early and accurate detection. In this study, we proposed DeepDrAMD, a hierarchical vision transformer-based deep learning model that in...

Few-shot out-of-distribution detection for automated screening in retinal OCT images using deep learning.

Scientific reports
Deep neural networks have been increasingly proposed for automated screening and diagnosis of retinal diseases from optical coherence tomography (OCT), but often provide high-confidence predictions on out-of-distribution (OOD) cases, compromising the...

Understanding and interpreting CNN's decision in optical coherence tomography-based AMD detection.

European journal of ophthalmology
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)
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).

Automated Age-Related Macular Degeneration Detector on Optical Coherence Tomography Images Using Slice-Sum Local Binary Patterns and Support Vector Machine.

Sensors (Basel, Switzerland)
Artificial intelligence has revolutionised smart medicine, resulting in enhanced medical care. This study presents an automated detector chip for age-related macular degeneration (AMD) using a support vector machine (SVM) and three-dimensional (3D) o...