Characterizing Age Effects on Wideband Absorbance in Normal-Hearing Children Via-Statistical and Machine Learning AnalysesOA

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.Methods:A 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.Results:Neither gender nor ear side had a sig-nificantly 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.Conclusion:WBA 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.