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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: Feb 28, 2026

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

Deep Learning-Based Prediction of Visual Field Mean Deviation from Numeric OCT Data in Glaucoma.

Shintaro Yasuda¹, Takanori Hasegawa², Sota Yoshimoto¹

¹Department of Ophthalmology, Institute of Science Tokyo, Tokyo, Japan.

Journal of Imaging Informatics in Medicine

|February 25, 2026

Summary

Deep learning models can predict visual field loss using optical coherence tomography (OCT) data. This artificial intelligence approach may improve glaucoma diagnosis by analyzing retinal thickness measurements.

Keywords:

Convolutional neural network Deep learning Glaucoma Optical coherence tomography Vision transformer Visual field prediction

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Last Updated: Feb 28, 2026

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Area of Science:

Ophthalmology
Artificial Intelligence
Medical Imaging

Background:

Glaucoma diagnosis relies on visual field testing, which can be subjective.
Optical coherence tomography (OCT) provides objective structural measurements of the retina.
Integrating OCT data with deep learning may enhance diagnostic accuracy.

Purpose of the Study:

To evaluate if deep learning (DL) models can predict Humphrey Visual Field Analyzer (HFA) 30-2 mean deviation (MD) using numeric OCT data.
To assess the performance of various DL models in predicting visual field status.
To determine the potential of DL in improving glaucoma diagnosis.

Main Methods:

A retrospective analysis of 1200 eyes (432 glaucoma, 768 normal) with spectral-domain OCT and HFA 30-2 testing.
Pixel-wise retinal thickness data from OCT were input into eight DL models (CNNs and Vision Transformers).
Two tasks were performed: regression for continuous MD prediction and classification for VF deterioration discrimination, evaluated using cross-validation.

Main Results:

The InceptionV3 model showed the best regression performance (MAE 3.36 dB, R² 0.53).
Convolutional neural network (CNN)-based models demonstrated high discrimination performance for early, moderate, and severe visual field loss.
An accurate and explainable DL system was developed for predicting HFA MD values from OCT data.

Conclusions:

Deep learning models can accurately predict Humphrey Visual Field Analyzer mean deviation using optical coherence tomography data.
This predictive DL approach shows promise for enhancing glaucoma diagnosis.
The developed system offers an explainable method for leveraging OCT data in clinical practice.