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Related Concept Videos

Glaucoma: Overview01:25

Glaucoma: Overview

987
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

Open Angle Glaucoma: Treatment

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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

Angle Closure Glaucoma: Treatment

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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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Identification of glaucoma from fundus images using deep learning techniques.

S Ajitha1, John D Akkara2, M V Judy1

  • 1Department of Computer Applications, Cochin University of Science and Technology, Kochi, Kerala, India.

Indian Journal of Ophthalmology
|September 28, 2021
PubMed
Summary

This study presents a deep learning algorithm for automatic glaucoma diagnosis using fundus images. The convolutional neural network (CNN) model achieved high accuracy, aiding in early detection and prevention of vision loss.

Keywords:
Artificial intelligenceconvolutional neural networksdeep learningglaucomasupport-vector machine

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Area of Science:

  • Ophthalmology
  • Computer Science
  • Artificial Intelligence

Background:

  • Glaucoma is a leading cause of irreversible blindness globally, often stemming from elevated intraocular pressure.
  • Early and accurate diagnosis of glaucoma is critical for preventing vision impairment.
  • Manual glaucoma detection requires specialized expertise and significant experience.

Purpose of the Study:

  • To develop and evaluate a convolutional neural network (CNN) algorithm for the automated diagnosis of glaucoma from fundus images.
  • To assess the diagnostic performance of the proposed CNN model in classifying images as glaucomatous or normal.

Main Methods:

  • A 13-layer CNN was trained on 1113 fundus images (660 normal, 453 glaucomatous) from multiple databases.
  • Data augmentation increased the training set to 12012 images, with a 70/20/10 split for training, validation, and testing.
  • The algorithm was implemented using Google Colab for accessibility and ease of use.

Main Results:

  • The CNN model with a SoftMax classifier achieved 93.86% accuracy, 85.42% sensitivity, and 100% specificity and precision.
  • A support vector machine (SVM) classifier integrated with the CNN model yielded superior results: 95.61% accuracy, 89.58% sensitivity, and 100% specificity and precision.

Conclusions:

  • The deep learning model demonstrates significant capability in identifying glaucoma from retinal fundus images.
  • The proposed system offers a potential tool for ophthalmologists to achieve faster, more accurate, and reliable glaucoma diagnoses.
  • This automated approach can aid in the early detection and management of glaucoma, mitigating vision loss.