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Glaucoma: Overview01:25

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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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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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Differentiating Glaucomatous Optic Neuropathy From Non-glaucomatous Optic Neuropathies Using Deep Learning

Mahsa Vali1, Massood Mohammadi2, Nasim Zarei2

  • 1From the Department of Electrical and Computer Engineering (M.V.), Isfahan University of Technology, Isfahan, Iran.

American Journal of Ophthalmology
|March 3, 2023
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Summary

A deep learning algorithm effectively distinguishes glaucomatous optic neuropathy (GON) from non-glaucomatous optic neuropathies (NGONs). This AI tool shows higher sensitivity than human specialists, offering promising results for diagnosing optic disc changes.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Differentiating glaucomatous optic neuropathy (GON) from non-glaucomatous optic neuropathies (NGONs) is crucial for accurate diagnosis and treatment.
  • Optic disc changes can be subtle and challenging to distinguish, necessitating advanced diagnostic tools.

Purpose of the Study:

  • To develop and evaluate a deep learning framework for differentiating glaucomatous optic disc changes (GON) from non-glaucomatous optic disc changes (NGONs).

Main Methods:

  • A deep-learning system was trained and validated on 2183 digital color fundus photographs, utilizing an optic disc segmentation network and transfer learning.
  • The system was tested on a single-center dataset and four external datasets to assess its classification performance for normal, GON, and NGON optic discs.

Main Results:

  • The best-performing algorithm, DenseNet121, achieved high sensitivity (95.36%) and specificity (92.19%) on the single-center dataset.
  • On external validation, the network demonstrated a sensitivity of 85.53% and specificity of 89.02% for differentiating GON from NGON, outperforming a glaucoma specialist's diagnostic accuracy.

Conclusions:

  • The proposed deep learning algorithm shows superior sensitivity compared to glaucoma specialists in differentiating GON from NGON.
  • The algorithm's strong performance on unseen data indicates its significant potential as a diagnostic aid in ophthalmology.