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Related Concept Videos

Visual Agnosia01:12

Visual Agnosia

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Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round...
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Vision01:24

Vision

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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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Visual Prostheses in the Era of Artificial Intelligence Technology.

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Artificial intelligence (AI) shows promise for improving visual prostheses by enhancing image processing and stimulation strategies. However, further clinical validation is crucial for real-world effectiveness in restoring vision.

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence
  • Ophthalmology

Background:

  • Technological advancements have enabled invasive visual prostheses, yet functional outcomes, particularly visual acuity, remain limited.
  • Retinal and cortical prostheses (RCPs) are emerging technologies aiming to restore vision.
  • Artificial intelligence (AI) presents a potential avenue for enhancing RCP performance.

Purpose of the Study:

  • To review current developments in retinal and cortical prostheses (RCPs).
  • To critically assess the role of AI in advancing visual prostheses.
  • To systematically review AI-driven image and signal processing for improved clinical outcomes.

Main Methods:

  • A systematic literature review was conducted using PubMed and Elicit.
  • 455 studies were screened, with 28 included for analysis.
  • Focus on AI applications in image saliency extraction and stimulation-perception consistency.

Main Results:

  • AI applications in RCPs primarily focus on saliency extraction and stimulation consistency.
  • Artificial neural networks were used in 14 of 28 studies, with 12 involving model training.
  • While 22 studies used empirical data, 15 relied on simulated prosthetic vision.

Conclusions:

  • AI algorithms demonstrate potential for optimizing prosthetic vision through improved image processing and stimulation.
  • Current research heavily relies on simulations, with limited validation in real-world settings.
  • Clinical validation with blind patients is essential to confirm the effectiveness of AI-enhanced visual prostheses.