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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...

Open Angle Glaucoma: Treatment

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.
Drugs such as carbonic anhydrase inhibitors, α2- and...

Angle Closure Glaucoma: Treatment

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 11, 2025

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

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

Published on: November 30, 2022

Predicting glaucoma progression using deep learning framework guided by generative algorithm.

Shaista Hussain¹, Jacqueline Chua^2,3, Damon Wong^2,4,5

¹Institute of High Performance Computing, A*STAR, Singapore, Singapore. hussains@ihpc.a-star.edu.sg.

Scientific Reports

|November 15, 2023

Summary

This summary is machine-generated.

This study developed a deep learning model using optical coherence tomography (OCT) and visual field (VF) data to predict glaucoma progression. The model accurately forecasts vision loss earlier than current methods, aiding customized patient treatment.

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Last Updated: Jul 11, 2025

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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

Published on: May 25, 2020

Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Glaucoma is a progressive optic neuropathy leading to blindness.
Accurate prediction of glaucoma progression is crucial for personalized treatment.
Current visual field (VF) testing is inconsistent, hindering reliable progression tracking.

Purpose of the Study:

To develop a multimodal deep learning model for predicting glaucoma progression.
To utilize optical coherence tomography (OCT) images and VF data for enhanced prediction accuracy.
To assess the efficacy of using synthetic OCT images for earlier glaucoma detection.

Main Methods:

A deep learning model combining convolutional neural networks (CNN) and long short-term memory (LSTM) was developed.
The model integrated OCT images, VF values, and clinical data from 86 glaucoma patients over 12 months.
Generative adversarial networks (GANs) were used to synthesize future OCT images for prediction.

Main Results:

The multimodal model achieved an AUC of 0.83 for predicting glaucoma progression 6 months earlier.
Utilizing synthetic future OCT images improved prediction accuracy, achieving an AUC of 0.81 for 9-month earlier prediction.
The model outperformed predictions based solely on structural (AUC=0.68) or functional (AUC=0.72) measures.

Conclusions:

The proposed deep learning approach effectively predicts glaucoma progression using multimodal data.
Synthetic OCT image generation enhances early detection capabilities for glaucoma.
This novel method offers a promising tool for timely intervention and management of glaucoma patients.