A systematic review of machine learning approaches in cochlear implant outcomes
Abstract Cochlear implants (CIs) have transformed the lives of over one million individuals with hearing impairment, including children as young as nine months. This systematic review critically examines the current literature on the application of machine learning (ML) techniques for predicting CI...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01733-9 |
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| author | Anu Prasad Sreenivasan Nair Srikanta K. Mishra Pedro Andres Alba Diaz |
| author_facet | Anu Prasad Sreenivasan Nair Srikanta K. Mishra Pedro Andres Alba Diaz |
| author_sort | Anu Prasad Sreenivasan Nair |
| collection | DOAJ |
| description | Abstract Cochlear implants (CIs) have transformed the lives of over one million individuals with hearing impairment, including children as young as nine months. This systematic review critically examines the current literature on the application of machine learning (ML) techniques for predicting CI outcomes. A comprehensive search identified 20 relevant studies. Imaging-based studies demonstrated high predictive accuracy for language and speech perception outcomes. Neural function measures provided a feasible way to assess the functional status of the auditory nerve, while clinical and audiological predictors were extensively explored through data mining techniques. Additionally, ML-based speech enhancement algorithms showed promise in improving speech recognition in noisy environments, a major challenge for CI users. Despite these advancements, a significant gap remains in developing models that can be directly integrated into CI programming. Integrating ML into CIs— in areas like signal processing and device programming—holds immense potential to support personalized patient care for hearing-impaired individuals. |
| format | Article |
| id | doaj-art-e6d4b69c50504da2bb13450ad8671b16 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-e6d4b69c50504da2bb13450ad8671b162025-08-20T03:42:00ZengNature Portfolionpj Digital Medicine2398-63522025-07-018111210.1038/s41746-025-01733-9A systematic review of machine learning approaches in cochlear implant outcomesAnu Prasad Sreenivasan Nair0Srikanta K. Mishra1Pedro Andres Alba Diaz2Department of Speech Language & Hearing Sciences, University of TexasDepartment of Speech Language & Hearing Sciences, University of TexasDepartment of Speech Language & Hearing Sciences, University of TexasAbstract Cochlear implants (CIs) have transformed the lives of over one million individuals with hearing impairment, including children as young as nine months. This systematic review critically examines the current literature on the application of machine learning (ML) techniques for predicting CI outcomes. A comprehensive search identified 20 relevant studies. Imaging-based studies demonstrated high predictive accuracy for language and speech perception outcomes. Neural function measures provided a feasible way to assess the functional status of the auditory nerve, while clinical and audiological predictors were extensively explored through data mining techniques. Additionally, ML-based speech enhancement algorithms showed promise in improving speech recognition in noisy environments, a major challenge for CI users. Despite these advancements, a significant gap remains in developing models that can be directly integrated into CI programming. Integrating ML into CIs— in areas like signal processing and device programming—holds immense potential to support personalized patient care for hearing-impaired individuals.https://doi.org/10.1038/s41746-025-01733-9 |
| spellingShingle | Anu Prasad Sreenivasan Nair Srikanta K. Mishra Pedro Andres Alba Diaz A systematic review of machine learning approaches in cochlear implant outcomes npj Digital Medicine |
| title | A systematic review of machine learning approaches in cochlear implant outcomes |
| title_full | A systematic review of machine learning approaches in cochlear implant outcomes |
| title_fullStr | A systematic review of machine learning approaches in cochlear implant outcomes |
| title_full_unstemmed | A systematic review of machine learning approaches in cochlear implant outcomes |
| title_short | A systematic review of machine learning approaches in cochlear implant outcomes |
| title_sort | systematic review of machine learning approaches in cochlear implant outcomes |
| url | https://doi.org/10.1038/s41746-025-01733-9 |
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