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.

中文翻译：

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使用机器学习分析正常人和耳朵中渗出的中耳炎的宽带吸收率

宽带吸收吸收（WAI）已有十多年的历史了，但是其临床应用仍然面临着对WAI结果的了解有限和解释不力的挑战。这项研究旨在开发机器学习（ML）工具，以识别正常中耳和渗出性中耳炎（OME）的中耳在不同频率压力区域的WAI吸收特征，从而能够自动诊断中耳疾病。在这项研究中，进行了数据分析，包括WAI数据的预处理，统计分析和分类模型开发，以及从2D频率-压力WAI图像中提取关键区域。我们的实验结果表明，机器学习工具似乎具有从WAI数据自动诊断中耳疾病的巨大潜力。