RESUMEN
In cochlear implant (CI) procedures, an electrode array is surgically inserted into the cochlea. The electrodes are used to stimulate the auditory nerve and restore hearing sensation for the recipient. If the array folds inside the cochlea during the insertion procedure, it can lead to trauma, damage to the residual hearing, and poor hearing restoration. Intraoperative detection of such a case can allow a surgeon to perform reimplantation. However, this intraoperative detection requires experience and electrophysiological tests sometimes fail to detect an array folding. Due to the low incidence of array folding, we generated a dataset of CT images with folded synthetic electrode arrays with realistic metal artifact. The dataset was used to train a multitask custom 3D-UNet model for array fold detection. We tested the trained model on real post-operative CTs (7 with folded arrays and 200 without). Our model could correctly classify all the fold-over cases while misclassifying only 3 non fold-over cases. Therefore, the model is a promising option for array fold detection.
RESUMEN
Purpose: Cochlear implants (CIs) use an array of electrodes surgically threaded into the cochlea to restore hearing sensation. Techniques for predicting the insertion depth of the array into the cochlea could guide surgeons toward more optimal placement of the array to reduce trauma and preserve the residual hearing. In addition to the electrode array geometry, the base insertion depth (BID) and the cochlear size could impact the overall array insertion depth. Approach: We investigated using these measurements to develop a linear regression model that can make preoperative or intraoperative predictions of the insertion depth of lateral wall CI electrodes. Computed tomography (CT) images of 86 CI recipients were analyzed. Using previously developed automated algorithms, the relative electrode position inside the cochlea was measured from the CT images. Results: A linear regression model is proposed for insertion depth prediction based on cochlea size, array geometry, and BID. The model is able to accurately predict angular insertion depths with a standard deviation of 41 deg and absolute deviation error of 32 deg. Conclusions: Surgeons may use this model for patient-customized selection of electrode array and/or to plan a BID for a given array that minimizes the likelihood of causing trauma to regions of the cochlea where residual hearing exists.