Centre for Speech and Language Therapy and Hearing Science, School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, CF5 2YB, UK.
Department of Otolaryngology, Guangzhou Women and Children's Medical Centre, Guangzhou City, Guangdong Province, 510623, China.
Sci Rep. 2021 May 20;11(1):10643. doi: 10.1038/s41598-021-89588-4.
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.
宽频声导抗（WAI）已经问世十余年了，但其临床应用仍然面临着人们对其结果理解有限且解读不佳的挑战。本研究旨在开发机器学习（ML）工具，以识别正常中耳和分泌性中耳炎（OME）中耳在不同频率-压力区域的 WAI 吸收率特征，从而实现中耳状况的自动诊断。数据分析包括 WAI 数据的预处理、统计分析和分类模型的开发，以及从 2D 频率-压力 WAI 图像中提取关键区域。实验结果表明，ML 工具似乎在从 WAI 数据自动诊断中耳疾病方面具有巨大的潜力。在 WAI 中识别出的关键区域为从业人员提供了更好地理解和解读 WAI 数据的指导，并为快速准确的诊断决策提供了可能。
