Mu Yi, Jiang Wen, Lin Huan, Yue Yuhong, Qiao Yuehua, Liu Wen
The Otolaryngology Department of the Affiliated Hospital of Xuzhou Medical University，Xuzhou，221000，China.
The Medical Technology College of Xuzhou Medical University.
Lin Chuang Er Bi Yan Hou Tou Jing Wai Ke Za Zhi. 2024 Mar;38(3):207-211;216. doi: 10.13201/j.issn.2096-7993.2024.03.005.
This study was to investigate the wideband acoustic immittance（WAI） characteristics of children with large vestibular aqueduct syndrome（LVAS） and to construct a diagnostic model for LVAS based on WAI and machine learning（ML） techniques. We performed a retrospective analysis of the data from 38 children（76 ears） with LVAS and 44 children（88 ears） with normal hearing. The data included conventional audiological examination, temporal bone CT scan and WAI test. We performed statistical analysis and developed multivariate diagnostic models based on different ML techniques. The two groups were balanced in terms of ear, gender, and age（>0.05）. The wideband absorbance（WBA） of the LVAS group was significantly lower than that of the control group at 1 000-2 519 Hz, while the WBA of the LVAS group was significantly higher than that of the control group at 4 000-6 349 Hz（<0.05）. WBA at 5 039 Hz under ambient pressure had a certain diagnostic value（AUC=0.767）. The multivariate diagnostic model had a high diagnostic value（AUC>0.8）, among which the KNN model performed the best（AUC=0.961）. The WAI characteristics of children with LVAS are significantly different from those of normal children. The diagnostic model based on WAI and ML techniques has high accuracy and reliability, and provides new ideas and methods for intelligent diagnosis of LVAS.
本研究旨在探讨大前庭导水管综合征（LVAS）患儿的宽带声导抗（WAI）特征，并基于WAI和机器学习（ML）技术构建LVAS的诊断模型。我们对38例（76耳）LVAS患儿和44例听力正常儿童（88耳）的数据进行了回顾性分析。数据包括常规听力学检查、颞骨CT扫描和WAI测试。我们进行了统计分析，并基于不同的ML技术开发了多变量诊断模型。两组在耳、性别和年龄方面均衡（>0.05）。LVAS组在1000 - 2519Hz的宽带吸收率（WBA）显著低于对照组，而LVAS组在4000 - 6349Hz的WBA显著高于对照组（<0.05）。常压下5039Hz的WBA具有一定的诊断价值（AUC = 0.767）。多变量诊断模型具有较高的诊断价值（AUC>0.8），其中KNN模型表现最佳（AUC = 0.961）。LVAS患儿的WAI特征与正常儿童有显著差异。基于WAI和ML技术的诊断模型具有较高的准确性和可靠性，为LVAS的智能诊断提供了新的思路和方法。
