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DOI: 10.1055/s-0042-1746815
MyCI – an app for individualized cochlear implantation
Authors
Cochlear implants provide effective auditory rehabilitation for many deaf or profound hearing loss patients. Many aspects such as duration of deafness, respective hearing loss, individual anatomy must be considered preoperatively in order to select an electrode and insertion depth that is optimal for the patient. For this purpose, an app was developed that takes these factors into account and provides doctors with an adequate support system.
For the development of this app, the data of approx. 1300 patients who were supplied with a MED-EL Electrode were evaluated. In these patients, the individual history, preoperative hearing threshold and speech comprehension were examined. In addition, the respective anatomy of the cochlea and the postoperative electrode position were measured in the radiological imaging (CB-CT). Postoperative hearing loss and speech understanding with cochlear implants were also included. Using machine learning, a model was developed that uses this data to make a prediction for a cochlear implantation. After development, the model was tested on 750 additional patients to check the prediction. It was possible to determine values for the expected postoperative hearing threshold depending on the type of electrode and insertion depth. The resulting selection of the electrode and insertion depth (virtual CI-OP) could be used in cases. Deviations arise in cases with difficult insertion or a drop in amplitude with cochlear monitoring to preserve residual hearing before reaching the desired insertion depth. MyCI offers a recommendation for the recommended electrode length or insertion depth and enables a prediction of the expected hearing loss and the expected speech results before cochlear implantation.
BMBF, MED-EL, HörSys
Conflict of Interest
The author declares that there is no conflict of interest.
Publication History
Article published online:
24 May 2022
© 2022. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).
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