Forecasting Spoken Language Development in Children with Cochlear Implants Using Preimplantation MRI

TL;DR

This study demonstrates that deep transfer learning models using preimplantation MRI data can accurately predict spoken language development outcomes in children with cochlear implants, outperforming traditional machine learning methods.

Contribution

The paper introduces a deep transfer learning approach that significantly improves prediction accuracy of language development in CI children using neuroanatomic MRI features.

Findings

DTL achieved 92.39% accuracy in predicting language outcomes.

DTL outperformed traditional ML models across all metrics.

The approach supports global application for language prognosis in CI children.

Abstract

Cochlear implants (CI) significantly improve spoken language in children with severe-to-profound sensorineural hearing loss (SNHL), yet outcomes remain more variable than in children with normal hearing. This variability cannot be reliably predicted for individual children using age at implantation or residual hearing. This study aims to compare the accuracy of traditional machine learning (ML) to deep transfer learning (DTL) algorithms to predict post-CI spoken language development of children with bilateral SNHL using a binary classification model of high versus low language improvers. A total of 278 implanted children enrolled from three centers. The accuracy, sensitivity and specificity of prediction models based upon brain neuroanatomic features using traditional ML and DTL learning. DTL prediction models using bilinear attention-based fusion strategy achieved: accuracy of 92.39% (95% CI, 90.70%-94.07%), sensitivity of 91.22% (95% CI, 89.98%-92.47%), specificity of 93.56% (95% CI, 90.91%-96.21%), and area under the curve (AUC) of 0.977 (95% CI, 0.969-0.986). DTL outperformed traditional ML models in all outcome measures. DTL was significantly improved by direct capture of discriminative and task-specific information that are advantages of representation learning enabled by this approach over ML. The results support the feasibility of a single DTL prediction model for language prediction of children served by CI programs worldwide.