Using Machine Learning for Analysis of Wideband Acoustic Immittance and Assessment of Middle Ear Function in Infants
Objectives: Evaluating 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,...
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MDPI AG
2025-03-01
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| Series: | Audiology Research |
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| Online Access: | https://www.mdpi.com/2039-4349/15/2/35 |
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| author | Shan Peng Yukun Zhao Xinyi Yao Huilin Yin Bei Ma Ke Liu Gang Li Yang Cao |
| author_facet | Shan Peng Yukun Zhao Xinyi Yao Huilin Yin Bei Ma Ke Liu Gang Li Yang Cao |
| author_sort | Shan Peng |
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| description | Objectives: Evaluating 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. Design: In 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. Results: The 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. Conclusions: This 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. |
| format | Article |
| id | doaj-art-b1e97f46e8a14bc69404ab448c9fb94f |
| institution | OA Journals |
| issn | 2039-4349 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Audiology Research |
| spelling | doaj-art-b1e97f46e8a14bc69404ab448c9fb94f2025-08-20T02:24:43ZengMDPI AGAudiology Research2039-43492025-03-011523510.3390/audiolres15020035Using Machine Learning for Analysis of Wideband Acoustic Immittance and Assessment of Middle Ear Function in InfantsShan Peng0Yukun Zhao1Xinyi Yao2Huilin Yin3Bei Ma4Ke Liu5Gang Li6Yang Cao7Department of Audiology and Speech Language Pathology, Department of Otorhinolaryngology-Head & Neck Surgery, West China Hospital of Sichuan University, Chengdu 610041, ChinaKey Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Wangjiang Road 29, Chengdu 610065, ChinaDepartment of Audiology and Speech Language Pathology, Department of Otorhinolaryngology-Head & Neck Surgery, West China Hospital of Sichuan University, Chengdu 610041, ChinaDepartment of Audiology and Speech Language Pathology, Department of Otorhinolaryngology-Head & Neck Surgery, West China Hospital of Sichuan University, Chengdu 610041, ChinaHealth Examination Center, Sichuan Electric Power Hospital, Chengdu 610011, ChinaKey Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Wangjiang Road 29, Chengdu 610065, ChinaDepartment of Audiology and Speech Language Pathology, Department of Otorhinolaryngology-Head & Neck Surgery, West China Hospital of Sichuan University, Chengdu 610041, ChinaKey Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Wangjiang Road 29, Chengdu 610065, ChinaObjectives: Evaluating 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. Design: In 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. Results: The 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. Conclusions: This 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.https://www.mdpi.com/2039-4349/15/2/35machine learningwideband acoustic immittancemiddle ear functionhearing of infants |
| spellingShingle | Shan Peng Yukun Zhao Xinyi Yao Huilin Yin Bei Ma Ke Liu Gang Li Yang Cao Using Machine Learning for Analysis of Wideband Acoustic Immittance and Assessment of Middle Ear Function in Infants Audiology Research machine learning wideband acoustic immittance middle ear function hearing of infants |
| title | Using Machine Learning for Analysis of Wideband Acoustic Immittance and Assessment of Middle Ear Function in Infants |
| title_full | Using Machine Learning for Analysis of Wideband Acoustic Immittance and Assessment of Middle Ear Function in Infants |
| title_fullStr | Using Machine Learning for Analysis of Wideband Acoustic Immittance and Assessment of Middle Ear Function in Infants |
| title_full_unstemmed | Using Machine Learning for Analysis of Wideband Acoustic Immittance and Assessment of Middle Ear Function in Infants |
| title_short | Using Machine Learning for Analysis of Wideband Acoustic Immittance and Assessment of Middle Ear Function in Infants |
| title_sort | using machine learning for analysis of wideband acoustic immittance and assessment of middle ear function in infants |
| topic | machine learning wideband acoustic immittance middle ear function hearing of infants |
| url | https://www.mdpi.com/2039-4349/15/2/35 |
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