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Related Concept Videos

Hearing

Hearing

When we hear a sound, our nervous system is detecting sound waves—pressure waves of mechanical energy traveling through a medium. The frequency of the wave is perceived as pitch, while the amplitude is perceived as loudness.

The Cochlea

The Cochlea

The cochlea is a coiled structure in the inner ear that contains hair cells—the sensory receptors of the auditory system. Sound waves are transmitted to the cochlea by small bones attached to the eardrum called the ossicles, which vibrate the oval window that leads to the inner ear. This causes fluid in the chambers of the cochlea to move, vibrating the basilar membrane.

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Predicting Hearing Aid Outcomes Using Machine Learning.

Pauline Roger¹, Thomas Lespargot², Catherine Boiteux¹

¹Amplifon France, Paris, France.

Audiology & Neuro-Otology

|February 3, 2025

Summary

Hearing aid (HA) fitting significantly improves speech understanding in quiet and noise. Key factors influencing HA success include technology choice, professional adjustments, and patient adherence, with binaural loudness balancing proving universally beneficial.

Keywords:

Big data Binaural loudness balancing Hearing aid benefit Hearing aids Machine learning

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Area of Science:

Audiology
Data Science in Healthcare
Hearing Science

Background:

Hearing aid (HA) fitting effectiveness for speech intelligibility in quiet and noise requires evaluation.
Identifying factors influencing HA outcomes is crucial for optimizing patient results.
Retrospective analysis of HA fittings from 2018-2021 at Amplifon centers will explore predictive factors.

Purpose of the Study:

To measure the effectiveness of hearing aid (HA) fitting on speech understanding in quiet and noise.
To identify significant factors influencing HA fitting outcomes.
To explore and classify predictive factors of HA outcomes, including technology, professional adjustments, and patient usage.

Main Methods:

Retrospective analysis of 77,661 HA users fitted between 2018 and 2021.
Utilized eXtreme Gradient Boosting machine learning to identify predictive factors of HA outcome.
Employed SHapley Additive exPlanations Value analysis to assess individual factor impact.

Main Results:

HA fitting significantly improves speech intelligibility in quiet and noise.
Outcomes are influenced by HA technology, fitting parameters (e.g., amplification, binaural loudness balancing), and therapy adherence.
Binaural loudness balancing demonstrated consistent benefits for all patients.

Conclusions:

Big data analysis is effective for evaluating predictive factors in HA outcomes.
Hearing care professionals play a vital role in maximizing patient outcomes through technology selection, fitting, and follow-up.
Results from heterogeneous populations require cautious interpretation and may benefit from patient clustering based on audiological profiles.