Wideband acoustic immittance characteristics and machine learning-based diagnostic model for children with large vestibular aqueduct syndrome
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.
large vestibular aqueduct syndromewideband acoustic immittancemachine learning
NETL
NSTL
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