AIMC Topic: Glaucoma

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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...

DB-SegNet: optimized framework for glaucoma detection and optic structure segmentation from retinal fundus images.

Scientific reports
Glaucoma remains one of the primary causes of irreversible blindness, characterized by gradual damage to the optic nerve, which often goes undetected until advanced stages. Accurate and early diagnosis depends heavily on precise segmentation of the o...

Real-world data landscape for glaucoma in Europe: a questionnaire-based analysis of resources among European Glaucoma Society members.

BMJ open ophthalmology
BACKGROUND/AIMS: To investigate the landscape to support Europe-wide collaborative real-world data (RWD) collection, exploring whether required resources are available to glaucoma clinicians.

Comparison of the number of peripapillary perforating scleral vessels between glaucomatous eyes and healthy eyes.

Scientific reports
This study aimed to compare the number of peripapillary perforating scleral vessels (PPSVs) between eyes with and without glaucoma. A retrospective case-control analysis was performed on patients with glaucoma and control participants who underwent s...

Multi-stage framework using transformer models, feature fusion and ensemble learning for enhancing eye disease classification.

Scientific reports
Eye diseases can affect vision and well-being, so early, accurate diagnosis is crucial to prevent serious impairment. Deep learning models have shown promise for automating the diagnosis of eye diseases from images. However, current methods mostly us...

Machine learning technology in the classification of glaucoma severity using fundus photographs.

Scientific reports
This study evaluates the performance of a machine learning model in classifying glaucoma severity using color fundus photographs. Glaucoma severity grading was based on the Hodapp-Parrish-Anderson (HPA) criteria incorporating the mean deviation value...

GAINSeq: glaucoma pre-symptomatic detection using machine learning models driven by next-generation sequencing data.

Scientific reports
Congenital glaucoma, a complex and diverse condition, presents considerable difficulties in its identification and categorization. This research used Next Generation Sequencing (NGS) whole-exome data to create a categorization framework using machine...

Analysis of 2-dimensional regional differences in the peripapillary scleral fibroblast cytoskeleton of normotensive and hypertensive mouse eyes.

Scientific reports
These studies aimed to study the mechanisms of glaucomatous peripapillary scleral (PPS) remodeling by investigating IOP-induced changes in fibroblast actin-collagen alignment and nuclear morphology in mouse PPS. Cryosections from the optic nerve head...

Advances in risk prediction models for Glaucoma: An updated narrative review.

Experimental eye research
Glaucoma is a leading cause of irreversible blindness and is characterized by optic nerve atrophy and progressive visual field loss. Risk prediction models are crucial for early screening and personalized treatment by identifying high-risk individual...