Analysing wideband absorbance immittance in normal and ears with otitis media with effusion using machine learning
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 charact...
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Nature Portfolio
2021
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oai:doaj.org-article:0fa1994683654faaab446e490daed3b22021-12-02T16:51:04ZAnalysing wideband absorbance immittance in normal and ears with otitis media with effusion using machine learning10.1038/s41598-021-89588-42045-2322https://doaj.org/article/0fa1994683654faaab446e490daed3b22021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89588-4https://doaj.org/toc/2045-2322Abstract 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 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.Emad M. GraisXiaoya WangJie WangFei ZhaoWen JiangYuexin CaiLifang ZhangQingwen LinHaidi YangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021) |
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Medicine R Science Q |
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Medicine R Science Q Emad M. Grais Xiaoya Wang Jie Wang Fei Zhao Wen Jiang Yuexin Cai Lifang Zhang Qingwen Lin Haidi Yang Analysing wideband absorbance immittance in normal and ears with otitis media with effusion using machine learning |
| description |
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 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. |
| format |
article |
| author |
Emad M. Grais Xiaoya Wang Jie Wang Fei Zhao Wen Jiang Yuexin Cai Lifang Zhang Qingwen Lin Haidi Yang |
| author_facet |
Emad M. Grais Xiaoya Wang Jie Wang Fei Zhao Wen Jiang Yuexin Cai Lifang Zhang Qingwen Lin Haidi Yang |
| author_sort |
Emad M. Grais |
| title |
Analysing wideband absorbance immittance in normal and ears with otitis media with effusion using machine learning |
| title_short |
Analysing wideband absorbance immittance in normal and ears with otitis media with effusion using machine learning |
| title_full |
Analysing wideband absorbance immittance in normal and ears with otitis media with effusion using machine learning |
| title_fullStr |
Analysing wideband absorbance immittance in normal and ears with otitis media with effusion using machine learning |
| title_full_unstemmed |
Analysing wideband absorbance immittance in normal and ears with otitis media with effusion using machine learning |
| title_sort |
analysing wideband absorbance immittance in normal and ears with otitis media with effusion using machine learning |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/0fa1994683654faaab446e490daed3b2 |
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