Journal of neural engineering
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In an increasingly data-driven world, artificial intelligence is expected to be a key tool for converting big data into tangible benefits and the healthcare domain is no exception to this. Machine learning aims to identify complex patterns in multi-dimensional data and use these uncovered patterns to classify new unseen cases or make data-driven predictions. ⋯ We aim to take some of the mystery out of machine learning and depict how machine learning models can be useful for medical applications. Finally, this tutorial provides a few practical suggestions for how to properly design a machine learning model for a generic medical problem.
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Cochleae of long-term cochlear implant users have shown evidence of particulate platinum (Pt) corroded from the surface of Pt electrodes. The pathophysiological effect of Pt within the cochlea has not been extensively investigated. We previously evaluated the effects of Pt corrosion at high charge densities and reported negligible pathophysiological impact. The present study extends this work by examining techniques that may reduce Pt corrosion. ⋯ Significant increases in tissue response and Pt corrosion were observed following stimulation at high charge densities. Charge recovery using CC, and to a lesser extent AP, reduced the amount of Pt corrosion but not the tissue response. Stimulation at change densities an order of magnitude higher than those used when programming cochlear implant recipients in the clinic, produced a vigorous tissue response and corrosion products without evidence of neural loss.