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Related Concept Videos

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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Predicting Glaucoma Development With Longitudinal Deep Learning Predictions From Fundus Photographs.

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Deep learning algorithms analyzing retinal nerve fiber layer thickness from fundus photos can predict glaucoma development. Longitudinal changes in these predictions identify high-risk patients needing closer monitoring.

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Glaucoma is a leading cause of irreversible blindness.
  • Early detection and monitoring of glaucoma progression are crucial for vision preservation.
  • Retinal nerve fiber layer (RNFL) thickness is a key indicator of glaucomatous damage.

Purpose of the Study:

  • To evaluate if deep learning (DL) algorithm predictions of RNFL thickness from fundus photographs can forecast future glaucomatous visual field defects.
  • To determine the predictive value of longitudinal changes in DL-based RNFL thickness estimates for glaucoma conversion.

Main Methods:

  • Retrospective cohort study of 1,072 eyes from 827 glaucoma-suspect patients with normal visual fields at baseline.
  • Fundus photographs and standard automated perimetry (SAP) were collected during a mean follow-up of 5.9 years.
  • An OCT-trained DL algorithm (M2M) estimated RNFL thickness; joint longitudinal survival models assessed prediction of glaucoma conversion.

Main Results:

  • 18% of eyes (196) converted to glaucoma.
  • The rate of RNFL thickness change predicted by M2M was significantly different between converters (-1.02 μm/y) and non-converters (-0.67 μm/y) (P < .001).
  • Both baseline RNFL thickness and its rate of change predicted glaucoma conversion (P < .001).

Conclusions:

  • Longitudinal RNFL thickness predictions from fundus photographs using DL algorithms can identify patients at high risk of glaucoma conversion.
  • This AI-driven approach offers a promising tool for predicting glaucoma development in at-risk individuals.