[Wideband acoustic immittance characteristics and machine learning-based diagnostic model for children with large vestibular aqueduct syndrome].
Lin Chuang Er Bi Yan Hou Tou Jing Wai Ke Za Zhi
; 38(3): 207-211;216, 2024 Mar.
Article
in Zh
| MEDLINE
| ID: mdl-38433688
ABSTRACT
Objective:
This study was to investigate the wideband acoustic immittanceï¼WAIï¼ characteristics of children with large vestibular aqueduct syndromeï¼LVASï¼ and to construct a diagnostic model for LVAS based on WAI and machine learningï¼MLï¼ techniques.Methods:
We performed a retrospective analysis of the data from 38 childrenï¼76 earsï¼ with LVAS and 44 childrenï¼88 earsï¼ 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.Results:
The two groups were balanced in terms of ear, gender, and ageï¼P>0.05ï¼. The wideband absorbanceï¼WBAï¼ 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 Hzï¼P<0.05ï¼. WBA at 5 039 Hz under ambient pressure had a certain diagnostic valueï¼AUC=0.767ï¼. The multivariate diagnostic model had a high diagnostic valueï¼AUC>0.8ï¼, among which the KNN model performed the bestï¼AUC=0.961ï¼.Conclusion:
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.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Vestibular Aqueduct
/
Vestibular Diseases
Limits:
Child
/
Humans
Language:
Zh
Journal:
Lin Chuang Er Bi Yan Hou Tou Jing Wai Ke Za Zhi
Year:
2024
Document type:
Article
Country of publication:
China