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Patient-specific computational models of retinal prostheses.

Kathleen E Kish1, Alex Yuan2, James D Weiland1

  • 1University of Michigan.

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Computational models predict visual perception thresholds for retinal prostheses. Retinal thickness beneath electrodes is a key factor, enabling personalized, in silico device programming for better outcomes in blind patients.

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Ophthalmology

Background:

  • Retinal prostheses aim to restore vision by stimulating inner retinal neurons.
  • Variability in electrode perception thresholds and phosphene characteristics complicates device programming.
  • Manual threshold determination is time-consuming and may not be optimal.

Conclusions:

  • Computational models, particularly patient-specific ones, can predict inter-electrode variability in retinal prostheses.
  • Retinal thickness is a significant predictor of perceptual threshold.
  • These models offer a promising approach for optimizing stimulation settings in silico, reducing clinical trial-and-error.