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Related Concept Videos

Vision01:24

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

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Related Experiment Video

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Visual Evoked Potential Recording in a Rat Model of Experimental Optic Nerve Demyelination
06:49

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Published on: July 29, 2015

Neural network-based diagnosing for optic nerve disease from visual-evoked potential.

Sadik Kara1, Ayşegül Güven

  • 1Department of Electrical and Electronics Eng., Erciyes University, 38039 Kayseri, Turkey. kara@erciyes.edu.tr

Journal of Medical Systems
|October 9, 2007
PubMed
Summary

This study introduces an Artificial Neural Network (ANN) for diagnosing optic nerve disease using visual evoked potential (VEP) signals. The ANN achieved high accuracy, demonstrating its potential for intelligent diagnostic assistance.

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Area of Science:

  • Ophthalmology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Optic nerve diseases pose significant diagnostic challenges.
  • Accurate and early diagnosis is crucial for effective treatment and vision preservation.
  • Current diagnostic methods can be invasive or require specialized equipment.

Purpose of the Study:

  • To develop a non-invasive diagnostic procedure for optic nerve disease.
  • To utilize Artificial Neural Networks (ANNs) for signal processing and classification of visual evoked potential (VEP) data.
  • To create an intelligent assistance system for diagnosing optic nerve conditions.

Main Methods:

  • Implementation of a multilayer feed forward Artificial Neural Network (ANN).
  • Training the ANN using the Levenberg-Marquardt backpropagation algorithm.
  • Classification of subjects into healthy and diseased categories based on VEP signals.

Main Results:

  • Achieved a correct classification rate of 96.87% for subjects with optic nerve disease.
  • Achieved a correct classification rate of 96.66% for healthy subjects.
  • Testing results aligned with diagnoses from physicians, angiography, VEP, and pattern electroretinography.

Conclusions:

  • The proposed ANN-based method shows high accuracy in diagnosing optic nerve disease from VEP signals.
  • The developed system demonstrates the potential for an intelligent diagnostic assistance tool.
  • This approach offers a promising non-invasive method for early detection and management of optic nerve disorders.