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<p>Cochlear implants (CIs) have transformed the lives of over one million individuals with hearing impairment, including children as young as nine months. This systematic review critically examines the current literature on the application of machine learning (ML) techniques for predicting CI outcomes. A comprehensive search identified 20 relevant studies. Imaging-based studies demonstrated high predictive accuracy for language and speech perception outcomes. Neural function measures provided a feasible way to assess the functional status of the auditory nerve, while clinical and audiological predictors were extensively explored through data mining techniques. Additionally, ML-based speech enhancement algorithms showed promise in improving speech recognition in noisy environments, a major challenge for CI users. Despite these advancements, a significant gap remains in developing models that can be directly integrated into CI programming. Integrating ML into CIs— in areas like signal processing and device programming—holds immense potential to support personalized patient care for hearing-impaired individuals.</p>

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A systematic review of machine learning approaches in cochlear implant outcomes

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月5日

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  • Anu Prasad Sreenivasan Nair,
  • Srikanta K. Mishra,
  • Pedro Andres Alba Diaz

摘要

Cochlear implants (CIs) have transformed the lives of over one million individuals with hearing impairment, including children as young as nine months. This systematic review critically examines the current literature on the application of machine learning (ML) techniques for predicting CI outcomes. A comprehensive search identified 20 relevant studies. Imaging-based studies demonstrated high predictive accuracy for language and speech perception outcomes. Neural function measures provided a feasible way to assess the functional status of the auditory nerve, while clinical and audiological predictors were extensively explored through data mining techniques. Additionally, ML-based speech enhancement algorithms showed promise in improving speech recognition in noisy environments, a major challenge for CI users. Despite these advancements, a significant gap remains in developing models that can be directly integrated into CI programming. Integrating ML into CIs— in areas like signal processing and device programming—holds immense potential to support personalized patient care for hearing-impaired individuals.