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dc.contributor.authorGrais, Emad M.
dc.contributor.authorWang, Xiaoya
dc.contributor.authorWang, Jie
dc.contributor.authorZhao, Fei
dc.contributor.authorJiang, Wen
dc.contributor.authorCai, Yuexin
dc.contributor.authorZhang, Lifang
dc.contributor.authorLin, Qinweng
dc.contributor.authorYang, Haidi
dc.identifier.citationGrais, E.M., Wang, X., Wang, J., Zhao, F., Jiang, W., Cai, Y., Zhang, L., Lin, Q. and Yang, H. (2021) 'Analysing wideband absorbance immittance in normal and ears with otitis media with effusion using machine learning', Scientific Reports, 11(1), pp.1-12.
dc.descriptionArticle published in Scientific Reports available open access at
dc.description.abstractWideband 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 included pre-processing of the WAI data, statistical analysis and classification model development, and key regions extraction from the 2D frequency-pressure WAI images. The 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.en_US
dc.relation.ispartofseriesScientific Reports;
dc.titleAnalysing wideband absorbance immittance in normal and ears with otitis media with effusion using machine learningen_US
rioxxterms.funderCardiff Metropolitan Universityen_US
rioxxterms.identifier.projectCardiff Metropolian (Internal)en_US

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