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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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Updated: Jul 7, 2026

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

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Published on: May 25, 2020

Multimodal Artificial Intelligence Models Predicting Glaucoma Progression Using Electronic Health Records and Retinal

Abigail Koornwinder¹, Youchen Zhang¹, Rohith Ravindranath¹

¹Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA, USA.

Translational Vision Science & Technology

|March 28, 2025

Summary

This summary is machine-generated.

Predicting glaucoma surgery needs is improved by combining electronic health records (EHRs) and retinal fiber layer optical coherence tomography (RNFL OCT) scans. A deep learning model (TabNet) integrating both data types showed superior performance for glaucoma progression prediction.

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Area of Science:

Ophthalmology
Medical Informatics
Artificial Intelligence

Background:

Glaucoma is a leading cause of irreversible blindness worldwide.
Accurate prediction of glaucoma progression is crucial for timely intervention and personalized treatment.
Current prediction methods may not fully leverage multimodal data.

Purpose of the Study:

To develop and compare predictive models for glaucoma surgery progression.
To integrate structured electronic health record (EHR) data with retinal fiber layer optical coherence tomography (RNFL OCT) imaging data.
To evaluate the performance of deep learning (TabNet) versus traditional machine learning (XGBoost) models.

Main Methods:

A cohort of 1472 glaucoma patients was analyzed from 2008-2023.
Models were trained using EHR data, RNFL OCT scans, or a fusion of both.
TabNet and XGBoost architectures were compared for predicting surgery within 12 months.

Main Results:

The TabNet fusion model achieved the highest predictive performance (AUROC = 0.832).
Fusion models integrating both EHR and RNFL data outperformed single-modality models.
The deep learning TabNet architecture outperformed the XGBoost model.

Conclusions:

Integrating multimodal data (EHR and RNFL OCT) significantly improves glaucoma progression prediction.
Deep learning models, specifically TabNet, demonstrate superior efficacy compared to traditional methods like XGBoost.
These predictive models can support clinical decision-making for personalized glaucoma management.