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Updated: Aug 14, 2025

Methodology for Biomimetic Chemical Neuromodulation of Rat Retinas with the Neurotransmitter Glutamate In Vitro
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Artificial intelligence techniques for retinal prostheses: a comprehensive review and future direction.

Chuanqing Wang1, Chaoming Fang1, Yong Zou2

  • 1Center of Excellence in Biomedical Research on Advanced Integrated-on-chips Neurotechnologies, School of Engineering, Westlake University, Hangzhou 310030, People's Republic of China.

Journal of Neural Engineering
|January 12, 2023
PubMed
Summary
This summary is machine-generated.

This review explores artificial intelligence algorithms for retinal prostheses, crucial for vision restoration in degenerative retinal diseases. It summarizes current methods and future directions for improved visual rehabilitation.

Keywords:
artificial intelligencebiophysical modelconvolution neural networkrecurrent neural networkretinal prosthesessaliency detection-based methodsspiking neural network.

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Area of Science:

  • Ophthalmology
  • Biomedical Engineering
  • Computer Science

Background:

  • Retinal prostheses offer vision restoration for diseases like age-related macular degeneration and retinitis pigmentosa.
  • The effectiveness of these devices relies on understanding retinal function and advancements in computer vision.

Purpose of the Study:

  • To review artificial intelligence (AI) techniques used in retinal prostheses.
  • To summarize computational frameworks derived from normal retinal function.
  • To analyze existing algorithms and propose future research directions.

Main Methods:

  • Investigated research on AI techniques for retinal prostheses.
  • Classified processing algorithms into computer vision-related methods, biophysical models, and deep learning models.
  • Illustrated normal and degenerated retinal structure and function.

Main Results:

  • Demonstrated vision rehabilitation mechanisms of three representative retinal prostheses.
  • Summarized the development and features of three distinct processing algorithm types.
  • Identified current limitations in algorithms and suggested future improvements.

Conclusions:

  • Systematically reviewed existing models for predicting retinal responses to stimuli.
  • Provided insights and future directions to inspire the design of enhanced retinal prosthesis algorithms.