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Related Concept Videos

Angle Closure Glaucoma: Treatment01:28

Angle Closure Glaucoma: Treatment

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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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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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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: Sep 9, 2025

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
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Fusing Structural Phenotypes with Functional Data for Early Prediction of Primary Angle-Closure Glaucoma Progression.

Swati Sharma1, Thanadet Chuangsuwanich2, Royston K Y Tan1,3

  • 1Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.

Arxiv
|September 2, 2025
PubMed
Summary
This summary is machine-generated.

This study developed a method to classify primary angle closure glaucoma (PACG) patients as slow or fast progressors. Combining optic nerve head (ONH) structure and visual field (VF) data improved classification accuracy for glaucoma progression.

Keywords:
Glaucoma progressionMachine learningOptical coherence tomographyPrimary angle closure glaucomaVisual field defects

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Area of Science:

  • Ophthalmology
  • Medical technology
  • Artificial intelligence in medicine

Background:

  • Primary angle closure glaucoma (PACG) is a leading cause of irreversible blindness worldwide.
  • Accurate classification of glaucoma progression is crucial for timely intervention and management.
  • Current methods for assessing glaucoma progression often lack integration of structural and functional parameters.

Purpose of the Study:

  • To develop and validate an integrated approach for classifying eyes as slow or fast progressors in patients with PACG.
  • To combine optic nerve head (ONH) structural features with sector-based visual field (VF) functional parameters for improved classification.
  • To identify key predictors of glaucoma progression using machine learning.

Main Methods:

  • A retrospective longitudinal study included PACG patients with at least 5 years of follow-up and 5 reliable VF tests.
  • Artificial intelligence (AI) segmented OCT volumes to extract 31 ONH parameters.
  • Sector-based VF data (mean sensitivity per region) were combined with structural parameters to train machine learning (ML) classifiers, with SHAP analysis for predictor identification.

Main Results:

  • The study analyzed 451 eyes from 299 PACG patients. Mean VFI progression was -0.92% per year.
  • A Random Forest model integrating structural and functional features achieved the highest classification performance (AUC = 0.87±0.02).
  • Key predictors included inferior ONH features (MRW, RNFL thickness), nasal-temporal LC curvature, and superior nasal VF sensitivity.

Conclusions:

  • Combining ONH structural and VF functional parameters significantly enhances the classification of glaucoma progression risk in PACG.
  • Inferior ONH features, specifically MRW and RNFL thickness, were identified as the most predictive parameters.
  • This integrated approach highlights the critical role of ONH morphology in monitoring PACG disease progression.