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Related Experiment Video

Updated: Aug 24, 2025

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
06:04

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

Published on: March 24, 2023

458

Machine Learning-Based Hearing Aid Fitting Personalization Using Clinical Fitting Data.

S I M M Raton Mondol1, Hyun Ji Kim2, Kyu Sung Kim2

  • 1Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Republic of Korea.

Journal of Healthcare Engineering
|October 26, 2022
PubMed
Summary

A new neural network (NN) algorithm optimizes hearing aid fittings by learning user preferences, reducing errors compared to standard methods. This approach enhances fitting satisfaction for individuals with hearing loss.

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Area of Science:

  • Audiology
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Standard hearing aid fitting software relies on formulas that may not align with individual user preferences.
  • Manual adjustments by audiologists are often necessary to optimize hearing aid settings for specific users.
  • A gap exists between initial software-based fittings and optimal user-specific requirements.

Purpose of the Study:

  • To develop a novel neural network (NN) algorithm for optimizing hearing aid fittings.
  • To minimize the discrepancy between prescribed hearing loss and user-preferred gain.
  • To create a fitting algorithm that learns individual user hearing preferences.

Main Methods:

  • Application of a neural network (NN) technique to create a fitting algorithm.
  • Training the NN with clinical fitting data, including hearing loss and preferred gain.
  • Evaluating the algorithm's performance using mean square error (MSE) with and without additional features.
  • Comparing the NN algorithm's performance against a commercial fitting software (Company A).

Main Results:

  • The simple NN algorithm achieved an average MSE of 5.4183%.
  • Incorporating additional features improved the NN algorithm's performance, reducing the average MSE to 5.2530%.
  • The NN algorithm outperformed Company A fitting software, which had the highest MSE at 5.4748%.

Conclusions:

  • The developed NN algorithm effectively learns user hearing preferences for optimized hearing aid fittings.
  • The NN approach demonstrates superior performance compared to existing commercial software, indicated by lower MSE.
  • This research offers a promising direction for improving hearing aid fitting satisfaction and benefiting the hearing-impaired community.