Explainable machine learning predictions of perceptual sensitivity for retinal prostheses.

ABSTRACT: Objective.Retinal prostheses evoke visual precepts by electrically stimulating functioning cells in the retina. Despite high variance in perceptual thresholds across subjects, among electrodes within a subject, and over time, retinal prosthesis users must undergo 'system fitting', a process performed to calibrate stimulation parameters according to the subject's perceptual thresholds. Although previous work has identified electrode-retina distance and impedance as key factors affecting thresholds, an accurate predictive model is still lacking.Approach.To address these challenges, we (1) fitted machine learning models to a large longitudinal dataset with the goal of predicting individual electrode thresholds and deactivation as a function of stimulus, electrode, and clinical p

SUBMITTER: Pogoncheff G 

PROVIDER: S-EPMC11144548 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature