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Related Concept Videos

Glaucoma: Overview01:25

Glaucoma: Overview

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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

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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...
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Angle Closure Glaucoma: Treatment01:28

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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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Glaucoma detection in myopic eyes using deep learning autoencoder-based regions of interest.

Christopher Bowd1, Akram Belghith1, Mark Christopher1

  • 1Hamilton Glaucoma Center and Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California (UC) San Diego, La Jolla, CA, United States.

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Summary

A dual autoencoder deep learning model accurately detected glaucoma in myopic eyes using optical coherence tomography (OCT) texture images. This advanced model outperformed traditional methods, offering a promising tool for glaucoma diagnosis.

Keywords:
artificial intelligenceclassificationdeep learningdiagnosisglaucomamyopiaoptical coherence tomography

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Glaucoma diagnosis in myopic eyes presents challenges.
  • Current diagnostic methods rely on metrics like retinal nerve fiber layer (RNFL) thickness.
  • Optical coherence tomography (OCT) provides detailed ocular imaging.

Purpose of the Study:

  • To evaluate a deep learning autoencoder model for glaucoma detection in myopic eyes.
  • To utilize regions of interest (ROI) from OCT texture enface images.
  • To compare the model's accuracy against traditional methods.

Main Methods:

  • A cross-sectional study included 453 eyes from 315 participants (healthy and glaucomatous).
  • Swept-source OCT (SS-OCT) imaging was used to construct texture enface images.
  • Four methods were compared: RNFL thickness, texture enface, single autoencoder, and dual autoencoder models.
  • Diagnostic accuracy was assessed using Area Under the Receiver Operating Curves (AUROC) and Area Under the Precision Recall Curves (AUPRC).

Main Results:

  • The dual autoencoder model achieved the highest AUROC (0.92) and AUPRC (0.86).
  • This model significantly outperformed single autoencoder, RNFL thickness, and texture enface models (p < 0.05).
  • No significant difference was found between RNFL thickness and texture enface measurements (p = 0.47).

Conclusions:

  • The dual autoencoder model demonstrated superior diagnostic accuracy for glaucoma in myopic eyes.
  • This deep learning approach, using ROI-based reconstruction error from OCT texture images, is a robust alternative to conventional metrics.
  • The findings suggest potential for enhanced glaucoma classification using advanced AI models.