Performance and reliability evaluation of an improved machine learning-based pure-tone audiometry with automated masking.

Journal: World journal of otorhinolaryngology - head and neck surgery
Published Date: Sep 12, 2024

Abstract

OBJECTIVE: Automated air-conduction pure-tone audiograms through Bayesian estimation and machine learning (ML) classification have recently been proposed in the literature. Although such ML-based audiometry approaches represent a significant addition to the field, they remain unsuited for daily clinical settings, in particular for listeners with asymmetric or conductive hearing loss, severe hearing loss, or cochlear dead zones. The goal here is to expand on previously proposed ML approaches and assess the performance of this improved ML audiometry for a large sample of listeners with a wide range of hearing status.

Authors

  • Nicolas Wallaert
    Department of Otorhinolaryngology-Head and Neck Surgery, Rennes University Hospital, Rennes, France.
  • Antoine Perry
    Laboratoire d'Informatique Signal et Image, Electronique et Télécommunications, ISEP Ecole d'ingénieurs du Numérique, Paris, France.
  • Sandra Quarino
    Department of Otorhinolaryngology-Head and Neck Surgery Rennes University Hospital Rennes France.
  • Hadrien Jean
    R&D Department, My Medical Assistant SAS, Reims, France.
  • Gwenaelle Creff
    Department of Otorhinolaryngology-Head and Neck Surgery, Rennes University Hospital, Rennes, France.
  • Benoit Godey
    Department of Otorhinolaryngology-Head and Neck Surgery, Rennes University Hospital, Rennes, France.
  • Nihaad Paraouty
    R&D Department, My Medical Assistant SAS, Reims, France.

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