Ensuring the correct positioning of the electrode array during cochlear implant surgery is crucial for achieving optimal results. Traditionally, the locations of the electrodes are assessed through radiological imaging after the surgery. However, electrical impedance measurements have recently emerged as a promising alternative for electrode localization. The aim of this study is to assess the performance of various machine learning algorithms to regress electrode locations using impedance telemetry alongside other data sources such as morphological parameters. We conducted a comprehensive performance analysis on a selection of different models and features in an evaluation dataset of 118 cases. This included 17 cases with up to two extracochlear electrodes. A final evaluation was performed on a hold-out dataset consisting of 13 cases. All cases used the same lateral wall electrode array with a length of 28 mm. Model performance was benchmarked against existing models, emphasizing those previously published. The best-performing model for predicting linear insertion depth (Extremely Randomized Trees) achieved a mean absolute error of 0.8 mm ± 0.6 mm (mean ± standard deviation) using leave-one-out cross-validation. We further reviewed the models in terms of feature importance and sensitivity to improve their interpretability and reliability. The gradient direction of the impedance matrix was found as one of the most important features. Our results demonstrate that our machine learning approach is superior to previous models and has potential for use in routine clinical practice. In future studies, it needs to be confirmed that the models can generalize to other, i.e., shorter or longer, electrode arrays.

Ensuring the correct positioning of the electrode array during cochlear implant surgery is crucial for achieving optimal results. Traditionally, the locations of the electrodes are assessed through radiological imaging after the surgery. However, electrical impedance measurements have recently emerged as a promising alternative for electrode localization. The aim of this study is to assess the performance of various machine learning algorithms to regress electrode locations using impedance telemetry alongside other data sources such as morphological parameters. We conducted a comprehensive performance analysis on a selection of different models and features in an evaluation dataset of 118 cases. This included 17 cases with up to two extracochlear electrodes. A final evaluation was performed on a hold-out dataset consisting of 13 cases. All cases used the same lateral wall electrode array with a length of 28 mm. Model performance was benchmarked against existing models, emphasizing those previously published. The best-performing model for predicting linear insertion depth (Extremely Randomized Trees) achieved a mean absolute error of 0.8 mm ± 0.6 mm (mean ± standard deviation) using leave-one-out cross-validation. We further reviewed the models in terms of feature importance and sensitivity to improve their interpretability and reliability. The gradient direction of the impedance matrix was found as one of the most important features. Our results demonstrate that our machine learning approach is superior to previous models and has potential for use in routine clinical practice. In future studies, it needs to be confirmed that the models can generalize to other, i.e., shorter or longer, electrode arrays.