Forecasting Spoken Language Development in Children With Cochlear Implants Using Preimplant Magnetic Resonance Imaging.

ABSTRACT:

Importance

Cochlear implants substantially improve spoken language in children with severe to profound sensorineural hearing loss, yet outcomes remain more variable than in children with healthy hearing. This variability cannot be reliably predicted for individual children using age at implant or residual hearing. Development of an artificial intelligence clinical tool to predict which patients will exhibit poorer improvements in language skills may enable an individualized approach to improve language outcomes.

Objective

To compare the accuracy of traditional machine learning (ML) with deep transfer learning (DTL) algorithms to predict post-cochlear implant spoken language development in children with bilateral sensorineural hearing loss using a binary classification model

SUBMITTER: Wang Y 

PROVIDER: S-EPMC12743310 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature