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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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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.
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Detection of Structural Glaucoma Progression with Deep Learning on Serial Optic Disc Photographs.

Vahid Mohammadzadeh1, Tyler Davis2, Esteban Morales1

  • 1Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.

Ophthalmology. Glaucoma
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A deep learning model effectively detects glaucoma progression using optic disc photographs, showing promising accuracy for clinical decision-making. This AI tool aids in identifying structural changes indicative of disease advancement.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Glaucoma is a leading cause of irreversible blindness worldwide.
  • Early detection and monitoring of glaucoma progression are crucial for timely intervention.
  • Serial optic disc photographs are key for assessing structural changes.

Purpose of the Study:

  • To design a supervised deep learning (DL) model for detecting glaucoma progression.
  • To utilize serial optic disc photographs (DPs) as input for the DL model.

Main Methods:

  • A retrospective longitudinal cohort study included 1,510 eyes from 916 patients with at least 2 years of follow-up.
  • A twin convolutional neural network (CNN) was developed to analyze pairs of DPs.
  • The dataset was split into training, validation, and testing sets (80/10/10 ratio).

Main Results:

  • The DL model achieved an Area Under the Curve (AUC) of 0.821 for detecting glaucoma progression.
  • The model demonstrated an overall accuracy of 72%, with 87% sensitivity and 68% specificity.
  • 22% of eyes showed deterioration based on clinical review.

Conclusions:

  • The developed twin CNN model can detect glaucoma progression with clinically relevant accuracy.
  • Deep learning shows potential as an adjunctive tool for clinical decision-making in identifying structural glaucoma progression.