  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.
