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Related Concept Videos

Glaucoma: Overview01:25

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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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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.
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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: Nov 9, 2025

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
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Predicting eyes at risk for rapid glaucoma progression based on an initial visual field test using machine learning.

Scott R Shuldiner1, Michael V Boland2, Pradeep Y Ramulu1

  • 1Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America.

Plos One
|April 16, 2021
PubMed
Summary
This summary is machine-generated.

Machine learning algorithms can predict rapid glaucoma progression using initial visual field tests. Older age and higher pattern standard deviation are key predictors, offering modest accuracy for identifying at-risk eyes.

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Area of Science:

  • Ophthalmology
  • Medical Artificial Intelligence
  • Glaucoma Research

Background:

  • Glaucoma is a leading cause of irreversible blindness worldwide.
  • Early detection of rapid glaucoma progression is crucial for timely intervention.
  • Visual field (VF) testing is a standard method for monitoring glaucoma, but predicting future progression remains challenging.

Purpose of the Study:

  • To evaluate the efficacy of machine learning algorithms (MLA) in predicting rapid glaucoma progression.
  • To determine if an initial visual field (VF) test is sufficient for predicting eyes at high risk of rapid progression.
  • To identify key features from initial VF tests that are associated with rapid glaucomatous changes.

Main Methods:

  • A retrospective analysis of longitudinal data from 14,217 patients with 175,786 VFs was conducted.
  • Machine learning models were trained using summary measures, reliability metrics, and age from initial VF tests.
  • A neural network model also incorporated point-wise threshold data; model performance was assessed using the area under the receiver operating curve (AUC).

Main Results:

  • The support vector machine model achieved an AUC of 0.72, demonstrating the best performance in predicting rapid progression (defined as MD worsening >1 dB/year) based on initial VF data.
  • Models trained on data from the first two VFs did not outperform models using only the initial VF.
  • Older age and higher pattern standard deviation on the initial VF were significantly associated with rapid progression.

Conclusions:

  • Machine learning algorithms can predict eyes at risk for rapid glaucoma progression with modest accuracy using only an initial visual field test.
  • The findings suggest that initial VF data, particularly age and pattern standard deviation, are valuable for risk stratification.
  • Future research could enhance predictive accuracy by integrating additional clinical data into the machine learning models.