@article{Qiu2026, 
author = {Jie Qiu and Chanfeng Shen and Yan Wang and Xiaohui Lai and Yi Mu and Fei Zhao and Wen Liu and Wen Jiang},
title = {Characterizing Age Effects on Wideband Absorbance in Normal-Hearing Children Via Statistical and Machine Learning Analyses},
year = {2026},
journal = {Journal of Otology},
volume = {21},
number = {3},
pages = {183-190},
keywords = {Wideband absorbance, Children, Middle-ear maturation, Machine learning, Normative data},
url = {https://www.sciopen.com/article/10.26599/JOTO.2026.9540071},
doi = {10.26599/JOTO.2026.9540071},
abstract = {Objective To characterize age-related changes in wideband absorbance (WBA) among normal-hearing children aged 0–6 years through combined statistical and machine learning analyses, and to establish developmental reference patterns supporting pediatric middle-ear diagnostics.MethodsA cross-sectional study was conducted on 579 children (1158 ears) categorized into five age groups. All participants passed age-appropriate hearing screenings. WBA was measured under both ambient pressure (AP) and tympanometric peak pressure (TPP) conditions across 16 frequencies (226–8000 Hz). Repeated-measures analysis of variance examined the effects of age, ear side, and gender, while Random Forest classifiers and principal component analysis (PCA) explored the discriminative structure and feature importance of WBA data.ResultsNeither gender nor ear side had a significantly effect on WBA patterns (p > 0.05). In constrast, Age significantly influenced WBA patterns (p < 0.001). Younger infants (< 6 months) exhibited dual-peaked “M-shaped” curves, whereas older children (3–6 years) showed single-peaked, inverted “U-shaped” profiles centered near 1600 Hz, reflecting progressive middle-ear maturation. The Random Forest model achieved a mean accuracy of 0.73 (balanced accuracy = 0.58), with the top-ranked predictors (AP_1000, and AP_793) emphasizing low-to-mid frequency absorbance and pressure-compensation effects as key age indicators. PCA with k-means clustering further revealed partially distinct groupings aligned with chronological age, supporting the developmental encoding of WBA responses.ConclusionWBA demonstrates distinct, age-dependent acoustic characteristics that correspond to physiological maturation of the middle ear. These findings provide a quantitative reference for pediatric wideband acoustic immittance and highlight the potential of machine learning in delineating developmental auditory patterns.}
}