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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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Related Experiment Video

Updated: Dec 11, 2025

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

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Published on: December 8, 2023

Artificial Intelligence Mapping of Structure to Function in Glaucoma.

Eduardo B Mariottoni^1,2, Shounak Datta³, David Dov³

¹Vision, Imaging and Performance (VIP) Laboratory, Duke Eye Center, Duke University, Durham, NC, USA.

Translational Vision Science & Technology

|August 21, 2020

Summary

This summary is machine-generated.

An AI-based structure-function map accurately predicts visual field loss from retinal nerve fiber layer damage. This tool enhances understanding of how spectral domain optical coherence tomography (SDOCT) detects damage on standard automated perimetry (SAP).

Keywords:

artificial intelligence deep learning glaucoma machine learning optical coherence tomography

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Published on: May 11, 2015

Area of Science:

Ophthalmology and Vision Science
Artificial Intelligence in Medicine
Medical Imaging Analysis

Background:

Glaucoma diagnosis and monitoring rely on correlating structural damage with functional visual field loss.
Spectral domain optical coherence tomography (SDOCT) measures retinal nerve fiber layer (RNFL) thickness, a key indicator of glaucomatous damage.
Standard automated perimetry (SAP) assesses visual field sensitivity, reflecting functional vision loss.

Purpose of the Study:

To develop an artificial intelligence (AI)-based structure-function (SF) map.
To correlate RNFL damage detected by SDOCT with functional loss measured by SAP.
To create a predictive model for visual field deficits based on SDOCT RNFL measurements.

Main Methods:

Utilized a large dataset of 26,499 SAP and SDOCT pairs from 15,173 glaucoma patients.
Trained a convolutional neural network (CNN) to predict SAP sensitivity thresholds from SDOCT RNFL thickness data.
Simulated localized RNFL defects to investigate topographic structure-function relationships.

Main Results:

The CNN achieved a strong correlation (0.60, P < 0.001) between predicted and measured SAP values, with a mean absolute error of 4.25 dB.
Simulated RNFL defects accurately predicted corresponding arcuate and paracentral visual field losses in the opposite hemifield.
Demonstrated a clear relationship between the location and depth of simulated RNFL defects and the resulting visual field deficits.

Conclusions:

A CNN can reliably predict visual field sensitivity thresholds from SDOCT RNFL thickness measurements.
The developed AI-based SF map effectively visualizes the translation of structural RNFL damage into functional SAP loss.
This AI tool enhances the understanding of glaucoma progression and provides valuable insights for clinical decision-making.