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Glaucoma: Overview

Glaucoma: Overview

Glaucoma is an eye condition characterized by increased intraocular pressure that damages the retina and optic nerve, leading to irreversible blindness if left untreated. The human eye has various components, including the cornea, iris, pupil, lens, and optic nerve. Aqueous humor is secreted by the epithelium of the ciliary body in the posterior chamber and flows through the trabecular meshwork and canal of Schlemm, maintaining normal intraocular pressure. The trabecular meshwork and the canal...

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Related Experiment Video

Updated: Jun 9, 2025

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

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A Novel Prediction Method for Glaucoma Detection Using Retinographies.

Bengie L Ortiz¹, Lance McMahon², Peter Ho³

¹Department of Pediatrics, Michigan Medicine, Ann Arbor, MI.

Colombian Caribbean Conference : (C3), IEEE

|October 28, 2024

Summary

This summary is machine-generated.

This study introduces a new model for detecting glaucoma, an eye disease damaging the optic nerve, using retinal images. The model achieved high accuracy in identifying glaucoma, offering a promising tool for early diagnosis and management.

Keywords:

3D mesh detection classification glaucoma image processing retinographies

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Last Updated: Jun 9, 2025

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Area of Science:

Ophthalmology
Medical Imaging
Computer Vision

Background:

Glaucoma is a leading cause of vision loss in the U.S., characterized by retinal nerve damage.
The progressive nature of glaucoma often makes early detection challenging for patients.
Effective management requires accurate and timely diagnostic solutions.

Purpose of the Study:

To develop and evaluate a novel detection model for glaucoma using retinography images.
To improve the accuracy and performance of glaucoma detection systems.
To identify key features for distinguishing healthy eyes from those with glaucoma.

Main Methods:

Utilized retinography images as input for a novel detection model.
Integrated and extracted features from 3D meshes for enhanced analysis.
Compared the performance of Classification Decision Trees (CDT), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms.

Main Results:

The proposed model achieved 100% accuracy in glaucoma detection using CDT and SVM.
The K-Nearest Neighbor (KNN) algorithm demonstrated 83.3% accuracy with the proposed model.
The methodology proved efficient in classifying retinographies for glaucoma detection.

Conclusions:

The novel detection model shows high efficacy in identifying glaucoma from retinographies.
The use of 3D mesh features significantly contributes to the model's performance.
This approach offers a valuable tool for early glaucoma diagnosis and patient management.