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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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Related Experiment Video

Updated: Oct 28, 2025

Subjective Refraction Test Using a Smartphone for Vision Screening
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Deep learning-assisted (automatic) diagnosis of glaucoma using a smartphone.

Kenichi Nakahara1, Ryo Asaoka2,3,4,5,6, Masaki Tanito7

  • 1Queue Inc, Tokyo, Japan.

The British Journal of Ophthalmology
|July 15, 2021
PubMed
Summary
This summary is machine-generated.

A deep learning algorithm shows high diagnostic ability for detecting glaucoma using smartphone fundus images. This artificial intelligence tool is particularly effective for screening advanced glaucoma cases.

Keywords:
glaucomaimaging

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Glaucoma diagnosis relies on expert interpretation of fundus photography.
  • Smartphone-based imaging offers potential for wider accessibility in glaucoma screening.
  • Validating AI algorithms for smartphone-acquired images is crucial for clinical adoption.

Purpose of the Study:

  • To validate a deep learning algorithm for diagnosing glaucoma using smartphone fundus photography.
  • To assess the diagnostic accuracy of the algorithm compared to traditional fundus cameras.

Main Methods:

  • A deep learning algorithm was trained on a large dataset of fundus photographs.
  • The algorithm was tested on images acquired using both a standard fundus camera and a smartphone.
  • Diagnostic accuracy was evaluated using the area under the receiver operating characteristic curve (AROC).

Main Results:

  • The algorithm achieved an AROC of 98.9% with fundus camera images and 84.2% with smartphone images.
  • For advanced glaucoma cases, the AROC was 99.3% with fundus cameras and 90.0% with smartphones.
  • Significant differences in AROC were observed between the two imaging modalities.

Conclusions:

  • A deep learning algorithm can effectively screen for glaucoma using smartphone-derived fundus photographs.
  • The algorithm demonstrates considerable diagnostic capability, especially for advanced glaucoma.
  • Smartphone-based AI screening holds promise for improving glaucoma detection rates.