Machine-Learning Predictions of Cochlear Implant Functional Outcomes: A Systematic Review.

Journal: Ear and hearing
Published Date: Jan 29, 2025

Abstract

OBJECTIVES: Cochlear implant (CI) user functional outcomes are challenging to predict because of the variability in individual anatomy, neural health, CI device characteristics, and linguistic and listening experience. Machine learning (ML) techniques are uniquely poised for this predictive challenge because they can analyze nonlinear interactions using large amounts of multidimensional data. The objective of this article is to systematically review the literature regarding ML models that predict functional CI outcomes, defined as sound perception and production. We analyze the potential strengths and weaknesses of various ML models, identify important features for favorable outcomes, and suggest potential future directions of ML applications for CI-related clinical and research purposes.

Authors

  • Jonathan T Mo
    University of California, Davis School of Medicine, Sacramento, California, USA.
  • Davis S Chong
    University of California, Davis School of Medicine, Sacramento, California, USA.
  • Cynthia Sun
    University of California, Davis School of Medicine, Sacramento, California, USA.
  • Nikita Mohapatra
    University of California, Davis School of Medicine, Sacramento, California, USA.
  • Nicole T Jiam
    Department of Otolaryngology - Head & Neck Surgery, University of California - San Francisco, San Francisco, California, USA.

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