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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...
Open Angle Glaucoma: Treatment01:27

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: Treatment01:28

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: Jul 17, 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

Trained artificial neural network for glaucoma diagnosis using visual field data: a comparison with conventional

Dimitrios Bizios1, Anders Heijl, Boel Bengtsson

  • 1Department of Clinical Sciences, Ophthalmology, Malmö University Hospital, Lund University, SE-205 02 Malmö, Sweden. dimitrios.bizios@med.lu.se

Journal of Glaucoma
|January 17, 2007
PubMed
Summary

An artificial neural network (ANN) accurately detects glaucoma visual field defects, outperforming other diagnostic algorithms. This confirms ANN

Related Experiment Videos

Last Updated: Jul 17, 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
  • Medical Diagnostics

Background:

  • Glaucoma is a leading cause of irreversible blindness worldwide.
  • Early detection of visual field defects is crucial for timely intervention and preventing vision loss.
  • Current diagnostic methods for visual field loss have limitations in accuracy and efficiency.

Purpose of the Study:

  • To evaluate the diagnostic performance of a trained artificial neural network (ANN) for recognizing glaucomatous visual field defects.
  • To compare the ANN's accuracy against established algorithms for detecting visual field loss.

Main Methods:

  • Utilized SITA Standard 30-2 visual field data from 100 glaucoma patients and 116 healthy controls.
  • Input data for the ANN comprised scored pattern deviation probability maps.
  • Compared ANN performance with Glaucoma Hemifield Test, Pattern Standard Deviation index, and a cluster-based algorithm.

Main Results:

  • The ANN achieved 93% sensitivity and 94% specificity (AUC=0.984) in detecting visual field defects.
  • The ANN demonstrated superior diagnostic accuracy compared to the Glaucoma Hemifield Test, Pattern Standard Deviation, and cluster algorithm.
  • The study included patients with early to moderate visual field loss (median MD=-6.16 dB).

Conclusions:

  • The ANN's high diagnostic performance was validated in an independent sample.
  • The ANN shows significant potential as a valuable clinical tool for glaucoma diagnosis.
  • Refined input data improved the ANN's accuracy in identifying glaucomatous visual field defects.