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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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Open Angle Glaucoma: Treatment

In open-angle glaucoma, the iridocorneal angle remains open, but the trabecular meshwork becomes stiff, slowing down the outflow of aqueous humor. This causes a buildup of aqueous humor in the anterior chamber, leading to a sudden increase in intraocular pressure. The treatment for open-angle glaucoma focuses on reducing the elevated intraocular pressure by either decreasing the secretion of aqueous humor or increasing its outflow.
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Angle Closure Glaucoma: Treatment

Angle-closure glaucoma, or closed-angle glaucoma, is an eye condition where the iris bulges out and blocks the iridocorneal angle, resulting in a buildup of aqueous humor and increased intraocular pressure. Immediate medical attention is necessary due to the sudden onset of symptoms. The treatment for angle-closure glaucoma includes short-term and long-term approaches. Short-term treatment involves using eye drops like pilocarpine to lower intraocular pressure by increasing aqueous humor...

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Related Experiment Video

Updated: Jul 29, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Automatic Diagnosis of Glaucoma from Retinal Images Using Deep Learning Approach.

Ayesha Shoukat¹, Shahzad Akbar¹, Syed Ale Hassan¹

¹Department of Computer Science, Riphah International University, Faisalabad Campus, Faisalabad 44000, Pakistan.

Diagnostics (Basel, Switzerland)

|May 27, 2023

Summary

Early glaucoma detection is crucial to prevent blindness. A new deep learning method accurately identifies subtle patterns in retinal images, aiding timely diagnosis and intervention.

Keywords:

augmentation deep learning early-stage detection fundus images glaucoma

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Area of Science:

Ophthalmology
Artificial Intelligence
Medical Imaging

Background:

Glaucoma, characterized by elevated intraocular pressure and optic nerve damage, can lead to irreversible blindness.
Early detection is vital, yet glaucoma is often diagnosed late in the elderly, necessitating improved diagnostic methods.
Current manual glaucoma assessment is time-consuming, costly, and requires specialized skills, with experimental techniques yet to offer a definitive solution.

Purpose of the Study:

To develop and validate an automated deep learning-based method for accurate early-stage glaucoma detection.
To identify subtle patterns in retinal images that may be overlooked by human clinicians.

Main Methods:

Utilized gray channels of fundus images for analysis.
Employed data augmentation to create a diverse dataset for training.
Trained a Convolutional Neural Network (CNN) model using the ResNet-50 architecture.

Main Results:

Achieved high accuracy in detecting early-stage glaucoma across multiple datasets (G1020, RIM-ONE, ORIGA, DRISHTI-GS).
On the G1020 dataset, the model demonstrated 98.48% accuracy, 99.30% sensitivity, 96.52% specificity, 97% AUC, and 98% F1-score.
The deep learning approach successfully identified patterns often missed in manual assessments.

Conclusions:

The proposed deep learning model offers a highly accurate and automated solution for early glaucoma detection.
This method has the potential to significantly aid clinicians in timely diagnosis and intervention, preventing vision loss.