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Related Concept Videos

Glaucoma: Overview01:25

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...
Anatomy of the Eyeball01:20

Anatomy of the Eyeball

The eye is a spherical, hollow structure composed of three tissue layers. The outer layer — the fibrous tunic, comprises the sclera — a white structure — and the cornea, which is transparent. The sclera encompasses some of the ocular surface, most of which is not visible. However, the 'white of the eye' is distinctively visible in humans compared to other species. The cornea, a clear covering at the front of the eye, enables light penetration. The eye's middle layer, the vascular tunic,...

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Related Experiment Video

Updated: Jun 28, 2026

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
07:11

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

Published on: May 25, 2020

Artificial neural network-based glaucoma diagnosis using retinal nerve fiber layer analysis.

D S Grewal1, R Jain, S P S Grewal

  • 1Grewal Eye Institute, Chandigarh, India. Dilraj@gmail.com

European Journal of Ophthalmology
|November 7, 2008
PubMed
Summary

An artificial neural network (ANN) effectively differentiates primary open angle glaucoma (POAG) from normal eyes using diverse clinical data. The ANN showed high sensitivity in identifying POAG but sometimes misclassified POAG suspects.

Related Experiment Videos

Last Updated: Jun 28, 2026

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
07:11

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

Published on: May 25, 2020

Area of Science:

  • Ophthalmology
  • Artificial Intelligence in Medicine
  • Medical Diagnostics

Background:

  • Glaucoma diagnosis relies on multiple tests, including clinical examination, visual fields, and imaging.
  • Accurate differentiation between normal eyes, primary open angle glaucoma (POAG) suspects, and POAG is crucial for timely intervention.
  • Artificial neural networks (ANNs) offer potential for integrating complex diagnostic data.

Purpose of the Study:

  • To develop and validate an artificial neural network (ANN) for classifying normal eyes, POAG suspects, and POAG patients.
  • To utilize a comprehensive dataset including clinical parameters, OCT, visual fields, and GDx analyzer outputs.
  • To assess the ANN's performance in differentiating glaucoma stages in an Asian-Indian population.

Main Methods:

  • An ANN model was developed using the EasyNN-plus simulator.
  • Input data included age, sex, myopia, IOP, and parameters from OCT, visual fields, and GDx.
  • One hundred eyes were classified into normal (n=35), POAG suspects (n=30), and POAG (n=35) groups.

Main Results:

  • With two outputs (POAG vs. normal), the ANN achieved 80% specificity and 93.3% sensitivity.
  • Ninety percent of POAG suspects were classified as abnormal.
  • For three outputs (normal, POAG suspect, POAG), the ANN achieved 65% overall classification, 60% specificity, and 71.4% sensitivity.
  • OCT parameters, particularly RNFL (Smax/Imax, Savg) and optic disc metrics (cup-area, cup-volume, vertical cup-disc ratio), were most important.

Conclusions:

  • An ANN integrating diverse diagnostic inputs can distinguish POAG from normal eyes and suspects.
  • The ANN demonstrated reasonable sensitivity for glaucoma detection.
  • A tendency for misclassifying POAG suspects as definite POAG was observed, indicating areas for model refinement.