AIMC Topic: Retinal Diseases

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Automated retinal disease classification using deep learning and AlexNet with statistical models analysis.

PloS one
Diabetic Retinopathy, Cataract, and Glaucoma are major retinal diseases that require early detection to prevent irreversible vision loss. This study proposes a deep learning-based framework for the automated classification of retinal images into four...

Intelligent retinal disease detection using deep learning.

Scientific reports
The rising prevalence of retinal diseases is a significant concern, as certain untreated conditions can lead to severe vision impairment or even blindness. Deep learning algorithms have emerged as a powerful tool for the diagnosis and analysis of med...

BayesRVAT enhances rare-variant association testing through Bayesian aggregation of functional annotations.

Genome research
Gene-level rare variant association tests (RVATs) are essential for uncovering disease mechanisms and identifying therapeutic targets. Advances in sequence-based machine learning have generated diverse variant pathogenicity scores, creating opportuni...

Benchmarking diffusion models against state-of-the-art architectures for OCT fluid biomarker segmentation.

PloS one
OBJECTIVES: Retinal diseases, major causes of vision impairment and blindness, are assessed using optical coherence tomography (OCT) scans. Automated report generation for retinal OCT scans, powered by deep learning, can help standardize interpretati...

A robust deep learning classifier for screening multiple retinal diseases on optical coherence tomography.

Scientific reports
Retinal diseases are among the leading causes of visual impairment worldwide, where timely diagnosis and management are critical to prevent irreversible vision loss and blindness, especially in regions with limited access to ophthalmologists. While a...

Enhanced retinal blood vessel segmentation via loss balancing in dense generative adversarial networks with quick attention mechanisms.

International ophthalmology
PURPOSE: Manual segmentation of retinal blood vessels in fundus images has been widely used for detecting vascular occlusion, diabetic retinopathy, and other retinal conditions. However, existing automated methods face challenges in accurately segmen...

Retinal image-based disease classification using hybrid deep architecture with improved image features.

International ophthalmology
OBJECTIVE: Ophthalmologists use retinal fundus imaging as a useful tool to diagnose retinal issues. Recently, research on machine learning has concentrated on disease diagnosis. However, disease detection is less accurate, more likely to be misidenti...

Multi-modal classification of retinal disease based on convolutional neural network.

Biomedical physics & engineering express
Retinal diseases such as age-related macular degeneration and diabetic retinopathy will lead to irreversible blindness without timely diagnosis and treatment. Optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) imag...

Advances in machine learning for ABCA4-related retinopathy: segmentation and phenotyping.

International ophthalmology
PURPOSE: Stargardt disease, also called ABCA4-related retinopathy (ABCA4R), is the most common form of juvenile-onset macular dystrophy and yet lacks an FDA approved treatment. Substantial progress has been made through landmark studies like that of ...