Analysing Wideband Absorbance Immittance in Normal and Ears with Otitis Media with Effusion Using Machine Learning

TL;DR

This study develops machine learning tools to analyze Wideband Absorbance Immittance data, aiming to improve automatic diagnosis of middle ear conditions like otitis media with effusion by identifying key features and regions.

Contribution

The paper introduces ML-based methods for interpreting WAI data, enhancing diagnostic accuracy and providing guidance for clinical interpretation of middle ear conditions.

Findings

ML tools show high potential for automated diagnosis

Key regions in WAI data aid interpretation

Achieved accurate classification of ear conditions

Abstract

Wideband Absorbance Immittance (WAI) has been available for more than a decade, however its clinical use still faces the challenges of limited understanding and poor interpretation of WAI results. This study aimed to develop Machine Learning (ML) tools to identify the WAI absorbance characteristics across different frequency-pressure regions in the normal middle ear and ears with otitis media with effusion (OME) to enable diagnosis of middle ear conditions automatically. Data analysis including pre-processing of the WAI data, statistical analysis and classification model development, together with key regions extraction from the 2D frequency-pressure WAI images are conducted in this study. Our experimental results show that ML tools appear to hold great potential for the automated diagnosis of middle ear diseases from WAI data. The identified key regions in the WAI provide guidance to practitioners to better understand and interpret WAI data and offer the prospect of quick and accurate diagnostic decisions.