Cross-Modal Cortical Activity in the Brain Can Predict Cochlear Implantation Outcome in Adults: A Machine Learning Study

Abstract

Objectives: Prediction of cochlear implantation (CI) outcome is often difficult because outcomes vary among patients. Though the brain plasticity across modalities during deafness is associated with individual CI outcomes, longitudinal observations in multiple patients are scarce. Therefore, we sought a prediction system based on cross-modal plasticity in a longitudinal study with multiple patients. METHODS: Classification of CI outcomes between excellent or poor was tested based on the features of brain cross-modal plasticity, measured using event-related responses and their corresponding electromagnetic sources. A machine learning estimation model was applied to 13 datasets from 3 patients based on linear supervised training. Classification efficiency was evaluated comparing prediction accuracy, sensitivity/specificity, total mis-classification cost, and training time among feature set conditions. Results: Combined feature sets with the sensor and source levels dramatically improved classification accuracy between excellent and poor outcomes. Specifically, the tactile feature set best explained CI outcome (accuracy, 98.83 ± 2.57%; sensitivity, 98.00 ± 0.01%; specificity, 98.15 ± 4.26%; total misclassification cost, 0.17 ± 0.38; training time, 0.51 ± 0.09 sec), followed by the visual feature (accuracy, 93.50 ± 4.89%; sensitivity, 89.17 ± 8.16%; specificity, 98.00 ± 0.01%; total misclassification cost, 0.65 ± 0.49; training time, 0.38 ± 0.50 sec). Conclusion: Individual tactile and visual processing in the brain best classified the current status when classified by combined sensor–source level features. Our results suggest that cross-modal brain plasticity due to deafness may provide a basis for classifying the status. We expect this novel method to contribute to the evaluation and prediction of CI outcomes.

Predicting Acoustic Hearing Preservation Following Cochlear Implant Surgery Using Machine Learning

“…These articles discuss a variety of uses for ML in CI such as speech processing optimization, intracochlear electrode location prediction, postoperative speech perception prediction, speech and language development prediction, predicting the benefits of bilateral CI, and predicting post-implantation quality of life. 4,[28][29][30][31][32][33] The purpose of this project was twofold. First, we sought to determine whether supervised ML methods can predict adult patients most at risk for loss of residual acoustic hearing following CI and determine clinical variables associated with hearing preservation (HP).…”

“…Crowson et al 27 showed an exponential growth in the number of publications pertaining to ML and CI between 2010 and 2018. These articles discuss a variety of uses for ML in CI such as speech processing optimization, intracochlear electrode location prediction, postoperative speech perception prediction, speech and language development prediction, predicting the benefits of bilateral CI, and predicting post‐implantation quality of life 4,28–33 …”