前庭导水管综合征儿童声能传递特点及机器学习模型构建
Wideband acoustic immittance characteristics and machine learning-based diagnostic model for children with large vestibular aqueduct syndrome
木怡 1蒋雯 2林欢 1岳昱宏 3乔月华 2刘稳2
作者信息
- 1. 徐州医科大学附属医院耳鼻咽喉科(江苏徐州,221000);徐州医科大学医学技术学院
- 2. 徐州医科大学附属医院耳鼻咽喉科(江苏徐州,221000);徐州医科大学医学技术学院;江苏省人工听觉工程实验室;徐州医科大学第二临床医学院
- 3. 徐州医科大学第二临床医学院
- 折叠
摘要
目的:探讨大前庭导水管综合征(large vestibular aqueduct syndrome,LVAS)儿童的声能传递特点,以及基于宽频声导抗(wideband acoustic immittance,WAI)和机器学习(machine learning,ML)技术的 LVAS 诊断模型构建.方法:回顾性分析38例(76耳)LVAS儿童和44例(88耳)听力正常儿童的病史、听力检查、颞骨CT扫描和WAI测试结果.对WAI可解释变量进行统计分析,并构建多变量诊断模型.结果:2组在耳别、性别、年龄等因素上差异均无统计学意义(P＞0.05).LVAS组在1 000～2 519 Hz的吸收率显著低于对照组,而在4 000～6 349 Hz的吸收率显著高于对照组(P＜0.05).WBA在5 039 Hz的环境压力下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K-Nearest Neighbor(KNN)模型表现最佳(AUC=0.961).结论:LVAS儿童的声能传递特点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WAI测试的智能化诊断提供了新思路和方法.
Abstract
Objective: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.Methods: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 audi-ological examination,temporal bone CT scan and WAI test.We performed statistical analysis and developed mult-ivariate diagnostic models based on different ML techniques.Results:The two groups were balanced in terms of ear,gender,and age(P＞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(P＜0.05).WBA at 5 039 Hz under ambient pressure had a certain di-agnostic 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).Conclusion:The WAI characteristics of children with LVAS are significantly different from those of normal children.The diagnostic model based on WAI and ML tech-niques has high accuracy and reliability,and provides new ideas and methods for intelligent diagnosis of LVAS.
关键词
大前庭导水管综合征/宽频声导抗/机器学习Key words
large vestibular aqueduct syndrome/wideband acoustic immittance/machine learning引用本文复制引用
基金项目
江苏省研究生科研与实践创新计划(KYCX23_2940)
出版年
2024
NETL
NSTL
万方数据