Prediction of Cochlear Implant Fitting by Machine Learning Techniques.

Objective: This study aimed to evaluate the differences in electrically evoked compound action potential (ECAP) thresholds and postoperative mapping current (T) levels between electrode types after cochlear implantation, the correlation between ECAP thresholds and T levels, and the performance of machine learning techniques in predicting postoperative T levels.

Study design: Retrospective case review.

Setting: Tertiary hospital.

Patients: We reviewed the charts of 124 ears of children with severe-to-profound hearing loss who had undergone cochlear implantation.

Interventions: We compared ECAP thresholds and T levels from different electrodes, calculated correlations between ECAP thresholds and T levels, and created five prediction models of T levels at switch-on and 6 months after surgery.

Main outcome measures: The accuracy of prediction in postoperative mapping current (T) levels.

Results: The ECAP thresholds of the slim modiolar electrodes were significantly lower than those of the straight electrodes on the apical side. However, there was no significant difference in the neural response telemetry thresholds between the two electrodes on the basal side. Lasso regression achieved the most accurate prediction of T levels at switch-on, and the random forest algorithm achieved the most accurate prediction of T levels 6 months after surgery in this dataset.

Conclusion: Machine learning techniques could be useful for accurately predicting postoperative T levels after cochlear implantation in children.