Neural features predict post-CI outcomes (Yuan et al., 2025)

<b>Purpose: </b>Our study used preoperative neuroanatomical features to predict auditory development in Chinese-learning children with cochlear implants (CIs).<b>Method: </b>T1-weighted whole-brain magnetic resonance imaging (MRI) scans were obtained from 17 Chinese-learning pediatric CI candidates (12 females and five males, age at MRI = 23.0 ± 15.0 months). Voxel-based morphometry was applied to examine the children’s whole-brain structure. Machine learning was employed using neuroanatomical features to predict children’s auditory skills up to 24 months after CI. The whole-brain neural model and auditory/visual cortex neural model were compared with a nonneural model using gender, age at CI activation, and preoperative residual hearing as predictors. Model performance was quantified using the mean square error (<i>MSE</i>) between predicted values and observations.<b>Results:</b> The model with preoperative neuroanatomical features showed a significantly smaller <i>MSE</i> than the nonneural model in predicting auditory skills in children with CIs. Specifically, the auditory-related area played an important role in predicting post-CI outcomes.<b>Conclusions: </b>The preoperative neuroanatomical features outperformed the nonneural features in predicting auditory skills in children with CIs. These results indicate that neural structure holds the potential to serve as an objective and effective feature for predicting post-CI outcomes.<b>Supplemental Material S1. </b>The mean (SD) of mean square error (MSE), root-mean-square error (RMSE), and mean absolute error (MAE), and the <i>p</i> values obtained from post hoc pairwise comparisons for MSE and RMSE.Yuan, D., Chang, W. T., Ng, I. H.-Y., Tong, M. C. F., Chu, W. C. W., Young, N. M., &amp; Wong, P. C. M. (2025). Predicting auditory skill outcomes after pediatric cochlear implantation using preoperative brain imaging. <i>American Journal of Audiology</i>, <i>34</i>(1), 51–59. https://doi.org/10.1044/2024_AJA-24-00139