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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...
Angle Closure Glaucoma: Treatment01:28

Angle Closure Glaucoma: Treatment

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: Jun 2, 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

Two-Stage Decoupling Framework for Variable-Length Glaucoma Prognosis.

Yiran Song1, Yikai Zhang1, Silvia Orengo-Nania1

  • 1University of Minnesota, Minneapolis MN, USA.

Learning with Longitudinal Medical Images and Data : First International Workshop, LMID 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 27, 2025, Proceedings. International Workshop on Learning with Longitudi
|June 1, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Two-Stage Decoupling Framework (TSDF) for glaucoma prognosis, improving predictions from variable-length patient data. The framework enhances early detection of irreversible blindness by effectively utilizing diverse datasets.

Keywords:
Attention-Based Temporal AggregationGlaucoma PrognosisSelf-Supervised LearningVariable-length Sequences

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Full-Circle Cauterization of Limbal Vascular Plexus for Surgically Induced Glaucoma in Rodents
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Full-Circle Cauterization of Limbal Vascular Plexus for Surgically Induced Glaucoma in Rodents

Published on: February 15, 2022

Related Experiment Videos

Last Updated: Jun 2, 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

Full-Circle Cauterization of Limbal Vascular Plexus for Surgically Induced Glaucoma in Rodents
10:10

Full-Circle Cauterization of Limbal Vascular Plexus for Surgically Induced Glaucoma in Rodents

Published on: February 15, 2022

Area of Science:

  • Ophthalmology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Glaucoma is a primary cause of irreversible blindness globally, necessitating accurate prognosis for timely intervention.
  • Current glaucoma prognosis methods face limitations due to fixed-length input constraints and challenges with small, heterogeneous datasets.
  • Existing end-to-end models struggle with data scarcity common in glaucoma research.

Purpose of the Study:

  • To develop a flexible and robust framework for glaucoma prognosis that accommodates variable-length sequential data.
  • To overcome limitations of fixed-length inputs and small dataset sizes in traditional glaucoma prediction models.
  • To improve the accuracy and efficiency of identifying patients at risk of glaucoma progression.

Main Methods:

  • Proposed a Two-Stage Decoupling Framework (TSDF) for variable-length glaucoma prognosis.
  • Stage 1: Utilized self-supervised learning for feature representation, aggregating diverse glaucoma datasets regardless of supervisory information.
  • Stage 2: Implemented an attention-based temporal aggregation module to process variable-length sequential inputs efficiently.

Main Results:

  • The TSDF demonstrated significant enhancement in model performance across two benchmark datasets (OHTS and GRAPE).
  • The framework proved effective and robust, even with datasets differing significantly in scale and clinical settings.
  • The approach maintained a compact parameter size while maximizing the utilization of all available patient data.

Conclusions:

  • The Two-Stage Decoupling Framework (TSDF) offers a flexible and effective solution for glaucoma prognosis using variable-length data.
  • The proposed method successfully addresses challenges posed by data heterogeneity and size limitations in glaucoma research.
  • This approach holds promise for improving early detection and intervention strategies to prevent glaucoma-induced blindness.