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Fast, Continuous Audiogram Estimation Using Machine Learning.

Xinyu D Song1, Brittany M Wallace, Jacob R Gardner

  • 11Laboratory of Sensory Neuroscience and Neuroengineering, Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA; 2Program in Audiology and Communication Sciences, Washington University in St. Louis, St. Louis, Missouri, USA; and 3Department of Computer Science & Engineering, Washington University in St. Louis, St. Louis, Missouri, USA.

Ear and Hearing
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Summary
This summary is machine-generated.

A new machine learning method accurately estimates hearing thresholds using pure-tone audiometry. This efficient technique provides continuous audiogram results, offering a reliable alternative for hearing assessments.

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Area of Science:

  • Audiology
  • Machine Learning in Healthcare
  • Signal Processing

Background:

  • Pure-tone audiometry is a standard hearing assessment method.
  • Traditional techniques determine thresholds by manipulating intensity at discrete frequencies.
  • Novel approaches are needed to improve efficiency and continuity of threshold estimation.

Purpose of the Study:

  • To evaluate a new nonparametric Bayesian estimation and machine learning classification method for audiogram threshold estimation.
  • To compare the accuracy and reliability of this novel technique against the conventional modified Hughson-Westlake procedure.

Main Methods:

  • Air conduction pure-tone audiometry was performed on 21 participants (18-90 years) with varied hearing abilities.
  • Two repetitions of automated machine learning audiogram estimation were conducted.
  • One repetition of the modified Hughson-Westlake procedure was used for comparison.

Main Results:

  • The machine learning method yielded threshold estimates comparable to the conventional technique (mean absolute difference: 4.16 ± 3.76 dB HL).
  • Repeated measurements of the machine learning method showed high reliability (mean absolute difference: 4.51 ± 4.45 dB HL).
  • The machine learning approach required fewer samples and produced a continuous threshold estimate.

Conclusions:

  • The novel machine learning technique accurately, reliably, and efficiently estimates continuous threshold audiograms.
  • This method is a promising tool for clinical and research audiometry.
  • The approach offers advantages over traditional methods in terms of sample efficiency and output continuity.