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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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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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Home
Deep Learning Models To Predict Primary Open-angle Glaucoma.

Home
Deep Learning Models To Predict Primary Open-angle Glaucoma.

Related Experiment Video

Full-Circle Cauterization of Limbal Vascular Plexus for Surgically Induced Glaucoma in Rodents

Full-Circle Cauterization of Limbal Vascular Plexus for Surgically Induced Glaucoma in Rodents

Published on: February 15, 2022

Deep learning models to predict primary open-angle glaucoma.

Ruiwen Zhou¹, J Philip Miller¹, Mae Gordon²

¹Division of Biostatistics, Washington University in St. Louis, School of Medicine, St. Louis, Missouri, USA.

Stat (International Statistical Institute)

|September 2, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces deep learning models to predict glaucoma conversion using longitudinal visual field data. A CNN-LSTM model showed the best performance in forecasting glaucoma progression.

Keywords:

convolutional neural network dynamic prediction landmark analysis long short-term memory

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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
Computer Science
Medical Data Analysis

Background:

Glaucoma is a leading cause of irreversible blindness globally.
Visual field (VF) testing is crucial for monitoring glaucoma progression.
Existing prediction methods often use single time points and binary classification, limiting accuracy.

Purpose of the Study:

To develop and evaluate deep learning models for predicting the time-to-glaucoma conversion.
To leverage longitudinal visual field data, incorporating temporal and spatial information.
To address limitations of binary classification in time-to-event prediction for glaucoma.

Main Methods:

Implementation of several deep learning approaches, including Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks.

Utilizing longitudinal visual field data from the Ocular Hypertension Treatment Study (OHTS) dataset.

Developing models that naturally handle temporal dependencies and spatial patterns in VF data.

Main Results:

The proposed CNN-LSTM model demonstrated superior performance compared to other examined models.
The models effectively incorporated temporal and spatial information from longitudinal VF data.
Accurate prediction of time-to-glaucoma conversion was achieved.

Conclusions:

Deep learning models, particularly CNN-LSTM, are effective for predicting glaucoma conversion using longitudinal visual field data.
Incorporating temporal and spatial dynamics improves the accuracy of glaucoma progression prediction.
This approach offers a more robust method for identifying individuals at risk of glaucoma.