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Related Experiment Video

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Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses
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Robust machine learning method for imputing missing values in audiograms collected in children.

Pittayapon Pitathawatchai1, Sitthichok Chaichulee2, Virat Kirtsreesakul1

  • 1Department of Otolaryngology Head and Neck Surgery, Faculty of Medicine, Prince of Songkla University, Hat Yai, Thailand.

International Journal of Audiology
|March 1, 2021
PubMed
Summary

A machine learning (ML) algorithm accurately predicts hearing loss audiograms in children. This ML approach is more reliable and precise than the common approach (CA) for predicting pediatric hearing thresholds.

Keywords:
Audiogramartificial intelligencecomputational audiologydigital hearing health carehearing-impaired childrenmachine learning

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Area of Science:

  • Audiology
  • Machine Learning in Healthcare
  • Pediatric Medicine

Background:

  • Accurate audiogram prediction is crucial for timely intervention in hearing-impaired children.
  • Current common approaches (CA) for predicting audiograms may lack precision.
  • Machine learning (ML) offers potential for improved diagnostic accuracy.

Purpose of the Study:

  • To evaluate the accuracy and reliability of an ML algorithm for predicting complete pediatric audiograms.
  • To compare the ML algorithm's performance against the common approach (CA).

Main Methods:

  • Retrospective study of 206 children with sensorineural hearing loss.
  • Nested cross-validation used to assess both CA and ML performance.
  • Six simulations performed, predicting missing audiogram thresholds using CA and ML.

Main Results:

  • ML algorithm demonstrated statistically significant lower median average absolute threshold differences (5-8 dBHL) compared to CA (6.25-10 dBHL) across all simulations (p < 0.05).
  • ML algorithm showed high reliability in predicting audiograms, with Cronbach's alphas (α) > 0.9 in all simulations.

Conclusions:

  • The ML algorithm is a reliable and more accurate method for predicting pediatric hearing loss audiograms.
  • ML surpasses the common approach in accuracy for predicting hearing thresholds in children.