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

Journal of otology Pub Date : 2026-07-27 eCollection Date: 2026-07-01 DOI:10.26599/JOTO.2026.9540071
Jie Qiu, Chanfeng Shen, Yan Wang, Xiaohui Lai, Yi Mu, Fei Zhao, Wen Liu, Wen Jiang
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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.

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 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.

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.

通过统计和机器学习分析表征年龄对正常听力儿童宽带吸光度的影响。
目的:通过统计学和机器学习相结合的方法分析0-6岁正常听力儿童宽带吸光度(WBA)的年龄相关性变化,建立儿童中耳诊断的发育参考模式。方法:对5个年龄组579例儿童(1158耳)进行横断面研究。所有参与者都通过了与年龄相适应的听力筛查。在16个频率(226- 8000hz)的环境压力(AP)和鼓室峰值压力(TPP)条件下测量WBA。重复测量方差分析检验了年龄、侧耳侧和性别的影响,而随机森林分类器和主成分分析(PCA)探讨了WBA数据的判别结构和特征重要性。结果:性别和耳侧对WBA型无显著影响(p < 0.05)。相比之下,年龄显著影响WBA模式(p < 0.001)。年龄较小的婴儿(< 6个月)表现为双峰“m型”曲线,而年龄较大的儿童(3-6岁)表现为单峰、以1600 Hz附近为中心的倒“u型”曲线,反映了中耳逐渐成熟。随机森林模型的平均精度为0.73(平衡精度为0.58),排名前几位的预测因子(AP_1000和AP_793)强调低频和中频吸光度和压力补偿效应是关键的年龄指标。基于k-means聚类的PCA进一步揭示了与实足年龄相关的部分不同分组,支持WBA反应的发育编码。结论:WBA表现出明显的年龄依赖性声学特征,与中耳的生理成熟相对应。这些发现为儿童宽带声阻抗提供了定量参考,并强调了机器学习在描述发育听觉模式方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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