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Related Concept Videos

Glaucoma: Overview01:25

Glaucoma: Overview

497
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...
497
Open Angle Glaucoma: Treatment01:27

Open Angle Glaucoma: Treatment

389
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...
389
Angle Closure Glaucoma: Treatment01:28

Angle Closure Glaucoma: Treatment

433
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...
433

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Related Experiment Video

Updated: May 30, 2025

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
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OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning.

Md Mahmudul Hasan1, Jack Phu2,3,4,5, Henrietta Wang2,3,5

  • 1School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia. md_mahmudul.hasan@unsw.edu.au.

Scientific Reports
|January 28, 2025
PubMed
Summary

This study introduces an explainable AI tool for glaucoma diagnosis using optical coherence tomography (OCT) images. The tool enhances diagnostic accuracy and provides transparent insights for eye care professionals.

Keywords:
Explainable machine learningGlaucomaOptical coherence tomographyPartial dependency analysisPerimetrySHAP analysis

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Glaucoma is a significant and growing global health concern.
  • Current automated glaucoma diagnosis relies on opaque deep learning models, hindering trust and clinical adoption.
  • Lack of explainability in AI diagnostic tools limits their clinical applicability and trustworthiness.

Purpose of the Study:

  • To develop and validate an explainable artificial intelligence (XAI) tool for glaucoma diagnosis and staging using optical coherence tomography (OCT) images.
  • To enhance the clinical applicability of AI in ophthalmology by providing interpretable diagnostic insights.
  • To improve the accuracy and reliability of automated glaucoma detection.

Main Methods:

  • Utilized optical coherence tomography (OCT) images from 334 normal and 268 glaucomatous eyes (categorized by severity).
  • Employed signal processing theory and rigorously evaluated model interpretability.
  • Developed a novel algorithm integrating SHapley Additive exPlanations (SHAP) for feature ranking and partial dependency analysis (PDA) for decision boundary estimation within machine learning (ML) models.

Main Results:

  • Machine learning models achieved high AUC values: 0.96 for early, 0.98 for moderate, and 1.00 for advanced glaucoma detection.
  • The developed XAI tool demonstrated superior diagnostic accuracy compared to clinicians, particularly in early-stage glaucoma (10.4-11.2% higher accuracy).
  • SHAP and PDA analyses provided global feature rankings and estimated decision boundary cut-offs, ensuring model transparency.

Conclusions:

  • The developed XAI tool offers a transparent and interpretable approach to glaucoma diagnosis and staging using OCT data.
  • This user-friendly software has the potential to significantly aid eye care practitioners in clinical decision-making.
  • The XAI tool surpasses traditional machine learning and clinician performance in early glaucoma detection, improving patient outcomes.