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Related Concept Videos

Glaucoma: Overview01:25

Glaucoma: Overview

595
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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Open Angle Glaucoma: Treatment01:27

Open Angle Glaucoma: Treatment

459
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...
459
Angle Closure Glaucoma: Treatment01:28

Angle Closure Glaucoma: Treatment

534
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...
534

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Related Experiment Video

Updated: Jul 11, 2025

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

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Deep-learning-based prediction of glaucoma conversion in normotensive glaucoma suspects.

Ahnul Ha1, Sukkyu Sun2, Young Kook Kim3,4

  • 1Department of Ophthalmology, Jeju National University, Jeju, Korea (the Republic of).

The British Journal of Ophthalmology
|November 2, 2023
PubMed
Summary
This summary is machine-generated.

Deep learning models accurately predict normal-tension glaucoma (NTG) conversion in glaucoma suspect (GS) patients using fundus images and clinical data. These models can forecast both the likelihood and timing of NTG development in individuals with normal intraocular pressure.

Keywords:
Glaucoma

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Glaucoma suspect (GS) patients with normal intraocular pressure (IOP) can convert to normal-tension glaucoma (NTG).
  • Accurate prediction of NTG conversion is crucial for timely intervention.
  • Current predictive methods may benefit from advanced computational approaches.

Purpose of the Study:

  • To evaluate the performance of deep learning (DL) models in predicting NTG conversion.
  • To assess the predictive capabilities of DL using both fundus images and clinical data.
  • To determine the optimal DL algorithms for predicting NTG development in GS patients.

Main Methods:

  • Utilized datasets of 12,458 GS eyes, including 210 eyes with a minimum 7-year follow-up and IOP < 21 mmHg.
  • Extracted features from optic disc and red-free retinal nerve fibre layer (RNFL) fundus images using convolutional autoencoders.
  • Employed XGBoost, Random Forest, and Gradient Boosting classifiers with combined fundus image and 15 clinical features (e.g., age, IOP, RNFL thickness, blood pressure).

Main Results:

  • All three machine learning algorithms demonstrated high diagnostic accuracy for NTG conversion prediction, with AUCs up to 0.994.
  • XGBoost exhibited superior performance in predicting the time to NTG conversion (mean squared error of 2.24).
  • Key clinical predictors for time-to-conversion included baseline IOP, diastolic blood pressure, and average circumpapillary RNFL thickness.

Conclusions:

  • Deep learning models integrating fundus images and clinical data show significant potential for predicting NTG conversion in normotensive GS patients.
  • These models can forecast both the occurrence and the timing of conversion to NTG.
  • The findings support the use of AI-driven tools for enhanced glaucoma risk assessment.