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The Use of Artificial Intelligence to Program Cochlear Implants.

Susan B Waltzman1, David C Kelsall2

  • 1Department of Otolaryngology-Head and Neck Surgery, NYU School of Medicine, New York, New York.

Otology & Neurotology : Official Publication of the American Otological Society, American Neurotology Society [And] European Academy of Otology and Neurotology
|March 17, 2020
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) programming for cochlear implants (CIs) shows results equivalent to experienced clinician methods. This AI approach offers standardization and improved access to CI care for patients.

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Area of Science:

  • Audiology
  • Medical technology
  • Artificial Intelligence in healthcare

Background:

  • Cochlear implant (CI) technology has advanced, but programming methods lack standardization.
  • Experienced clinician (EC) programming is the current standard but can be variable.

Purpose of the Study:

  • To compare the performance of an Artificial Intelligence (AI) based programming algorithm (FOX) against traditional EC programming methods in CI users.
  • To evaluate the efficacy and patient satisfaction of AI-driven CI programming.

Main Methods:

  • A prospective, nonrandomized, multicenter study with a within-subject design.
  • Fifty-five adult CI users with Nucleus sound processors participated.
  • CNC word and AzBio sentence tests in noise were administered using both EC and FOX programming methods, with subjective satisfaction measures.

Main Results:

  • Group mean performance was equivalent between EC and FOX programming methods (p=0.934).
  • While individual results varied, the majority of patients reported equivalent performance and preferred the FOX system.
  • Subjective patient satisfaction was measured and compared between the two methods.

Conclusions:

  • On average, AI-based FOX programming yields outcomes comparable to traditional EC programming techniques.
  • The FOX programming method has the potential to standardize CI programming across centers.
  • This AI approach may increase access to beneficial CI care for more individuals.