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Related Concept Videos

Glaucoma: Overview01:25

Glaucoma: Overview

494
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...
494
Open Angle Glaucoma: Treatment01:27

Open Angle Glaucoma: Treatment

384
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...
384
Angle Closure Glaucoma: Treatment01:28

Angle Closure Glaucoma: Treatment

426
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...
426

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Related Experiment Video

Updated: Jun 19, 2026

Laser Capture Microdissection of Highly Pure Trabecular Meshwork from Mouse Eyes for Gene Expression Analysis
13:47

Laser Capture Microdissection of Highly Pure Trabecular Meshwork from Mouse Eyes for Gene Expression Analysis

Published on: June 3, 2018

Deep Learning in Glaucoma Detection and Progression Prediction: A Systematic Review and Meta-Analysis.

Xiao Chun Ling1,2,3, Henry Shen-Lih Chen1, Po-Han Yeh4

  • 1Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou, Taoyuan 333, Taiwan.

Biomedicines
|February 26, 2025
PubMed
Summary

Deep learning (DL) excels at diagnosing glaucoma using fundus photography and OCT imaging. Future DL models need multimodal data for better glaucoma progression prediction.

Keywords:
artificial intelligencedeep learningdiagnosisfundus photographyglaucomaoptical coherence tomographyprogression

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Related Experiment Videos

Last Updated: Jun 19, 2026

Laser Capture Microdissection of Highly Pure Trabecular Meshwork from Mouse Eyes for Gene Expression Analysis
13:47

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Published on: June 3, 2018

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

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Published on: May 25, 2020

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Published on: February 15, 2022

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Glaucoma diagnosis and progression prediction are critical for preventing vision loss.
  • Deep learning (DL) shows promise in analyzing medical images for various diagnostic tasks.

Purpose of the Study:

  • To systematically evaluate the diagnostic and prognostic performance of DL algorithms in glaucoma detection and progression prediction.
  • To assess DL performance using fundus photography and optical coherence tomography (OCT) imaging.

Main Methods:

  • A comprehensive literature search was conducted across major databases up to October 2024.
  • A meta-analysis using a bivariate random-effects model was performed to pool performance metrics.
  • Key metrics included pooled sensitivity, specificity, likelihood ratios, and AUROC.

Main Results:

  • DL demonstrated high accuracy in glaucoma diagnosis via fundus photography (AUROC 0.90) and OCT (AUROC 0.86).
  • DL performance in predicting glaucoma progression was less robust compared to diagnostic tasks.
  • Internal validation datasets yielded higher accuracy than external ones.

Conclusions:

  • DL algorithms are highly effective for glaucoma diagnosis using fundus photography and OCT.
  • Integrating multimodal data and extensive real-world validation are crucial for improving DL-based glaucoma progression prediction.