ArticleAudiology research2025
Using Machine Learning for Analysis of Wideband Acoustic Immittance and Assessment of Middle Ear Function in Infants.
Article in Audiology research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Characterizing Age Effects on Wideband Absorbance in Normal-Hearing Children Via Statistical and Machine Learning Analyses.Journal of otology · 2026Article
- Machine learning-enhanced behavioural approach to detecting reactions to sound in infants and toddlers: proof-of-concept study.International journal of audiology · 2026Article
- Eustachian Tube Dysfunction in Hearing Loss: Mechanistic Pathways to Targeted Interventions.Biomedicines · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectivesEvaluating middle ear function is essential for interpreting screening results and prioritizing diagnostic referrals for infants with hearing impairments. Wideband Acoustic Immittance (WAI) technology offers a comprehensive approach by utilizing sound stimuli across various frequencies, providing a deeper understanding of ear physiology. However, current clinical practices often restrict WAI data analysis to peak information at specific frequencies, limiting its comprehensiveness.
designIn this study, we developed five machine learning models-feedforward neural network, convolutional neural network, kernel density estimation, random forest, and support vector machine-to extract features from wideband acoustic immittance data collected from newborns aged 2-6 months. These models were trained to predict and assess the normalcy of middle ear function in the samples.
resultsThe integrated machine learning models achieved an average accuracy exceeding 90% in the test set, with various classification performance metrics (accuracy, precision, recall, F1 score, MCC) surpassing 0.8. Furthermore, we developed a program based on ML models with an interactive GUI interface. The software is available for free download.
conclusionsThis study showcases the capability to automatically diagnose middle ear function in infants based on WAI data. While not intended for diagnosing specific pathologies, the approach provides valuable insights to guide follow-up testing and clinical decision-making, supporting the early identification and management of auditory conditions in newborns.
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Registered trials
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