Machine Learning Feasibility in Cochlear Implant Speech Perception Outcomes-Moving Beyond Single Biomarkers for Cochlear Implant Performance Prediction.

Journal: Ear and hearing
Published Date: Apr 4, 2025

Abstract

OBJECTIVES: Machine learning (ML) is an emerging discipline centered around complex pattern matching and large data-based prediction modeling and can improve precision medicine healthcare. Cochlear implants (CI) are highly effective, however, outcomes vary widely, and accurately predicting speech perception performance outcomes between patients remains a challenge. This study aims to evaluate the ability of ML to predict speech perception performance among CI recipients at 6-month post-implantation using only preoperative variables on one of the largest CI datasets to date, with an emphasis placed on identification of poor performers.

Authors

  • Matthew A Shew
    Otology & Neurotology, Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine in St. Louis, 660 South Euclid Avenue, PO Box 8115, St Louis, MO 63110, USA. Electronic address: mshew@wustl.edu.
  • Cole Pavelchek
    Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA.
  • Andrew Michelson
    Institute for Informatics, Data Science, and Biostatistics, Washington University School of Medicine, St. Louis, Missouri, USA.
  • Amanda Ortmann
    Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine, St. Louis, Missouri, USA.
  • Shannon Lefler
    Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine, St. Louis, Missouri, USA.
  • Amit Walia
    Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine, St. Louis, Missouri, USA.
  • Nedim Durakovic
    Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine, St. Louis, Missouri, USA.
  • Alisa Phillips
    Department of Otolaryngology-Head and Neck Surgery, Oregon Health and Science University, Portland, Oregon, USA.
  • Ayna Rejepova
    Department of Otolaryngology-Head and Neck Surgery, Oregon Health and Science University, Portland, Oregon, USA.
  • Jacques A Herzog
    Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine, St. Louis, Missouri, USA.
  • Phillip Payne
    Institute for Informatics, Data Science, and Biostatistics, Washington University School of Medicine, St. Louis, Missouri, USA.
  • Jay F Piccirillo
    Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine, St. Louis, Missouri, USA.
  • Craig A Buchman
    Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine, St. Louis, Missouri, USA.

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