Peng Shan, Zhao Yukun, Yao Xinyi, Yin Huilin, Ma Bei, Liu Ke, Li Gang, Cao Yang
Department of Audiology and Speech Language Pathology, Department of Otorhinolaryngology-Head & Neck Surgery, West China Hospital of Sichuan University, Chengdu 610041, China.
Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Wangjiang Road 29, Chengdu 610065, China.
Audiol Res. 2025 Mar 31;15(2):35. doi: 10.3390/audiolres15020035.
Evaluating middle ear function is essential for interpreting screening results and prioritizing diagnostic referrals for infants with hearing impairments. Wideband Acoustic Immittance (WAI) technology offers a comprehensive approach by utilizing sound stimuli across various frequencies, providing a deeper understanding of ear physiology. However, current clinical practices often restrict WAI data analysis to peak information at specific frequencies, limiting its comprehensiveness.
In this study, we developed five machine learning models-feedforward neural network, convolutional neural network, kernel density estimation, random forest, and support vector machine-to extract features from wideband acoustic immittance data collected from newborns aged 2-6 months. These models were trained to predict and assess the normalcy of middle ear function in the samples.
The integrated machine learning models achieved an average accuracy exceeding 90% in the test set, with various classification performance metrics (accuracy, precision, recall, F1 score, MCC) surpassing 0.8. Furthermore, we developed a program based on ML models with an interactive GUI interface. The software is available for free download.
This study showcases the capability to automatically diagnose middle ear function in infants based on WAI data. While not intended for diagnosing specific pathologies, the approach provides valuable insights to guide follow-up testing and clinical decision-making, supporting the early identification and management of auditory conditions in newborns.
评估中耳功能对于解读听力筛查结果以及确定听力受损婴儿的诊断转诊优先级至关重要。宽带声导抗（WAI）技术通过利用不同频率的声音刺激提供了一种全面的方法，能更深入地了解耳部生理学。然而，当前的临床实践往往将WAI数据分析局限于特定频率的峰值信息，限制了其全面性。
在本研究中，我们开发了五种机器学习模型——前馈神经网络、卷积神经网络、核密度估计、随机森林和支持向量机——以从2至6个月大新生儿收集的宽带声导抗数据中提取特征。这些模型经过训练，用于预测和评估样本中耳功能的正常情况。
集成的机器学习模型在测试集中的平均准确率超过90%，各种分类性能指标（准确率、精确率、召回率、F1分数、马修斯相关系数）超过0.8。此外，我们基于机器学习模型开发了一个带有交互式图形用户界面的程序。该软件可免费下载。
本研究展示了基于WAI数据自动诊断婴儿中耳功能的能力。虽然该方法并非用于诊断特定病症，但为指导后续测试和临床决策提供了有价值的见解，有助于新生儿听觉疾病的早期识别和管理。
