Explainable Machine Learning Predictions of Perceptual Sensitivity for Retinal Prostheses.

Autor:	Pogoncheff G; Department of Computer Science, University of California, Santa Barbara., Hu Z; Department of Computer Science, University of California, Santa Barbara., Rokem A; Department of Psychology and the eScience Institute, University of Washington, WA., Beyeler M; Department of Computer Science, University of California, Santa Barbara; Department of Psychological & Brain Sciences, University of California, Santa Barbara.
Jazyk:	angličtina
Zdroj:	MedRxiv : the preprint server for health sciences [medRxiv] 2023 Feb 10. Date of Electronic Publication: 2023 Feb 10.
DOI:	10.1101/2023.02.09.23285633
Abstrakt:	To provide appropriate levels of stimulation, retinal prostheses must be calibrated to an individual's perceptual thresholds ('system fitting'), despite thresholds varying drastically across subjects, across electrodes within a subject, and over time. Although previous work has identified electrode-retina distance and impedance as key factors affecting thresholds, an accurate predictive model is still lacking. To address these challenges, we 1) fitted machine learning (ML) models to a large longitudinal dataset with the goal of predicting individual electrode thresholds and deactivation as a function of stimulus, electrode, and clinical parameters ('predictors') and 2) leveraged explainable artificial intelligence (XAI) to reveal which of these predictors were most important. Our models accounted for up to 77% of the perceptual threshold response variance and enabled predictions of whether an electrode was deactivated in a given trial with F1 and AUC scores of up to 0.740 and 0.913, respectively. Deactivation and threshold models identified novel predictors of perceptual sensitivity, including subject age, time since blindness onset, and electrode-fovea distance. Our results demonstrate that routinely collected clinical measures and a single session of system fitting might be sufficient to inform an XAI-based threshold prediction strategy, which may transform clinical practice in predicting visual outcomes. Competing Interests: Competing Interests The authors were collaborators with Second Sight Medical Products, Inc. (now Vivani Medical, Inc.), the company that developed, manufactured, and marketed the Argus II Retinal Prosthesis System referenced within this article. Second Sight had no role in study design, data analysis, decision to publish, or preparation of the manuscript.
Databáze:	MEDLINE
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