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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...

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Related Experiment Video

Updated: May 20, 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

Progression of patterns (POP): a machine classifier algorithm to identify glaucoma progression in visual fields.

Michael H Goldbaum1, Intae Lee, Giljin Jang

  • 1Hamilton Glaucoma Center, Department of Ophthalmology, University of California at San Diego, La Jolla, CA 92093, USA. mgoldbaum@ucsd.edu

Investigative Ophthalmology & Visual Science
|July 13, 2012
PubMed
Summary
This summary is machine-generated.

Progression of Patterns (POP) effectively identifies glaucoma visual field defects, showing comparable results to other methods in suspects and outperforming Guided Progression Analysis in glaucoma and progressive optic neuropathy cases.

Related Experiment Videos

Last Updated: May 20, 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
  • Medical Technology
  • Machine Learning in Healthcare

Background:

  • Glaucomatous visual field (VF) defects can be challenging to detect early.
  • Accurate identification of VF progression is crucial for timely intervention.

Purpose of the Study:

  • To evaluate the efficacy of Progression of Patterns (POP) in identifying glaucomatous visual field (VF) defects.
  • To compare POP's performance against established methods like Visual Field Index (VFI), Mean Deviation (MD), and Guided Progression Analysis (GPA).

Main Methods:

  • POP utilizes a variational Bayesian independent component mixture model (VIM) machine learning classifier.
  • VIM analyzed Swedish Interactive Thresholding Algorithm (SITA) VFs, identifying seven glaucomatous patterns.
  • Progression was assessed using linear regression slopes on VIM axes, establishing a degree of confidence (DOC) for progression detection.

Main Results:

  • POP identified a similar number of progressing eyes as VFI, MD, and GPA in suspect cases.
  • POP identified significantly more progressing eyes than GPA in both glaucoma and progressive glaucomatous optic neuropathy (PGON) cohorts.
  • Statistical analysis confirmed POP's superior performance (P=0.01 for glaucoma, P=0.05 for PGON) compared to GPA.

Conclusions:

  • POP provides valuable information on the degree of confidence (DOC) for VF progression.
  • POP's ability to identify specific progressing VF defect patterns enhances clinical decision-making.
  • POP offers a valuable tool for clinicians in detecting VF progression in glaucoma patients.