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Related Concept Videos

Glaucoma: Overview01:25

Glaucoma: Overview

623
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...
623
Open Angle Glaucoma: Treatment01:27

Open Angle Glaucoma: Treatment

481
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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Automated Glaucoma Screening and Diagnosis Based on Retinal Fundus Images Using Deep Learning Approaches: A

Mohammad J M Zedan1,2, Mohd Asyraf Zulkifley1, Ahmad Asrul Ibrahim1

  • 1Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.

Diagnostics (Basel, Switzerland)
|July 14, 2023
PubMed
Summary
This summary is machine-generated.

Deep learning algorithms show promise for early glaucoma detection from retinal images. This systematic review analyzes 52 studies, highlighting AI

Keywords:
cup–disc ratio (CDR)deep learningglaucoma screening and diagnosisoptic nerve head (ONH)retinal diseaseretinal fundus image

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Glaucoma is a chronic eye condition leading to irreversible vision loss due to optic nerve damage, often caused by elevated intraocular pressure.
  • Early diagnosis and treatment are crucial for managing glaucoma and preventing vision impairment.
  • Current diagnostic methods rely on skilled ophthalmologists interpreting retinal images, which can be subjective and time-consuming.

Purpose of the Study:

  • To systematically review and analyze 52 recent studies on deep learning algorithms for glaucoma screening and diagnosis.
  • To evaluate datasets, performance metrics, and methodologies used in AI-based glaucoma detection.
  • To compare the strengths and weaknesses of various deep learning approaches in analyzing retinal fundus images.

Main Methods:

  • Systematic literature review of 52 state-of-the-art research articles.
  • Analysis of algorithms focusing on image pre-processing, localization, classification, and segmentation of retinal structures.
  • Evaluation of datasets, performance metrics, and imaging modalities employed in deep learning models for glaucoma diagnosis.

Main Results:

  • Deep learning algorithms demonstrate significant potential in accurately screening and diagnosing glaucoma from retinal fundus images.
  • The review identified various approaches and datasets used in developing these AI diagnostic tools.
  • Comparative analysis highlighted the strengths and limitations of different deep learning techniques for glaucoma detection.

Conclusions:

  • Automated glaucoma diagnosis using deep learning algorithms offers a promising avenue for improving diagnostic accuracy and efficiency.
  • AI-powered systems can assist ophthalmologists, potentially leading to earlier detection and better patient outcomes.
  • Further research and validation are essential to integrate these advanced diagnostic tools into clinical practice.