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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...
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  3. Deep Learning In Glaucoma Referral: Performance Assessment Using A Real-world Setting.
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  3. Deep Learning In Glaucoma Referral: Performance Assessment Using A Real-world Setting.

Related Experiment Video

A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss
07:12

A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss

Published on: April 11, 2025

Deep learning in glaucoma referral: Performance assessment using a real-world setting.

Afonso Lima-Cabrita1,2,3, Rafael Correia Barão1,3, Diogo Bernardo Matos1,3

  • 1Ophthalmology Department, Unidade Local de Saúde Lisboa Santa Maria, Lisbon, Portugal.

Acta Ophthalmologica
|June 27, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

A deep learning model accurately identified all glaucoma cases among referred patients, potentially reducing unnecessary referrals by over a third. This AI tool shows promise as a pre-referral filter in glaucoma screening.

Keywords:
artificial intelligencedeep learningglaucomaglaucoma referralophthalmology

Related Experiment Videos

A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss
07:12

A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss

Published on: April 11, 2025

Area of Science:

  • Ophthalmology
  • Artificial Intelligence in Healthcare
  • Medical Imaging Analysis

Background:

  • Glaucoma diagnosis relies on specialist evaluation, leading to high referral rates.
  • Current referral pathways can be inefficient, with a low prevalence of glaucoma in referred patients.
  • Developing automated screening tools is crucial for optimizing healthcare resource allocation.

Purpose of the Study:

  • To assess the efficacy of a deep learning (DL) model as a pre-referral filter for detecting referable glaucoma.
  • To evaluate the model's performance using colour fundus photographs (CFPs) compared to expert clinical judgment.

Main Methods:

  • A retrospective cohort study included 96 patients referred for glaucoma evaluation.
  • A DL model analyzed CFPs, with a simulated referral triggered if the output exceeded a threshold of 0.73.
  • Model predictions were compared against glaucoma diagnoses made by expert clinicians.

    • Main Results:

    • The DL model achieved 100% sensitivity in identifying glaucoma cases.
    • A model-led referral system could have reduced overall referrals by 37.5% without missing any glaucoma diagnoses.
    • The model demonstrated a specificity of 0.65 and a positive predictive value of 0.41.

    Conclusions:

    • The deep learning model accurately identified all glaucoma patients, demonstrating its potential to streamline referrals.
    • Implementing this AI-driven system could significantly reduce unnecessary specialist consultations.
    • The model's performance supports its use as an effective filter in the pre-referral stage for glaucoma care.