Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Spatial classification of glaucomatous visual field loss

D B Henson1, S E Spenceley, D R Bull

  • 1Department of Ophthalmology, University of Manchester.

The British Journal of Ophthalmology
|June 1, 1996
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Noise creates polarization artefacts.

Bioinspiration & biomimetics·2017
Same author

New superior-inferior visual field asymmetry indices for detecting POAG and their agreement with the glaucoma hemifield test.

Eye (London, England)·2015
Same author

Individualised patient care as an adjunct to standard care for promoting adherence to ocular hypotensive therapy: an exploratory randomised controlled trial.

Eye (London, England)·2011
Same author

Pascal panretinal laser ablation and regression analysis in proliferative diabetic retinopathy: Manchester Pascal Study Report 4.

Eye (London, England)·2011
Same author

High-resolution hyperspectral imaging of the retina with a modified fundus camera.

Journal francais d'ophtalmologie·2010
Same author

Preliminary survey of educational support for patients prescribed ocular hypotensive therapy.

Eye (London, England)·2010

This study developed an artificial neural network (ANN) to classify glaucoma visual field loss patterns. The ANN successfully categorized defects, aiding in monitoring disease progression.

Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Glaucoma is a leading cause of irreversible blindness.
  • Accurate classification of visual field loss is crucial for monitoring disease progression.
  • Current methods for classifying visual field patterns can be subjective.

Purpose of the Study:

  • To develop an objective classification system for spatial patterns of visual field loss in glaucoma.
  • To utilize artificial neural networks (ANNs) for automated classification of visual field defects.
  • To create a system capable of distinguishing between superior and inferior visual field loss patterns.

Main Methods:

  • Trained an artificial neural network (ANN), specifically a Kohonen self-organising feature map (SOM), using 560 Humphrey visual field analyser (program 24-2) records.

Related Experiment Videos

  • Configured the SOM to categorize visual field defects into 25 classes of superior and 25 classes of inferior visual field loss.
  • Arranged the 50 classes into two 5x5 maps for visualization and analysis.

    • Main Results:

    • The SOM successfully classified visual field defects based on their spatial patterns.
    • The generated maps demonstrated a continuum of visual field loss, from early to advanced stages.
    • The classification system effectively distinguished between different patterns of visual field impairment.

    Conclusions:

    • Artificial neural networks (ANNs) are capable of classifying visual field data based on loss patterns.
    • The trained ANN can be applied to longitudinal visual field data for monitoring glaucoma progression.
    • This objective classification system holds potential value for clinical management and research in glaucoma.