Comparing linear and non-linear models to estimate the appropriate cochlear implant electrode array length-are current methods precise enough?
- PMID: 37466660
- PMCID: PMC10764384
- DOI: 10.1007/s00405-023-08064-z
Comparing linear and non-linear models to estimate the appropriate cochlear implant electrode array length-are current methods precise enough?
Abstract
Purpose: In cochlear implantation with flexible lateral wall electrode arrays, a cochlear coverage (CC) range between 70% and 80% is considered ideal for optimal speech perception. To achieve this CC, the cochlear implant (CI) electrode array has to be chosen according to the individual cochlear duct length (CDL). Here, we mathematically analyzed the suitability of different flexible lateral wall electrode array lengths covering between 70% and 80% of the CDL.
Methods: In a retrospective cross-sectional study preoperative high-resolution computed tomography (HRCT) from patients undergoing cochlear implantation was investigated. The CDL was estimated using an otosurgical planning software and the CI electrode array lengths covering 70-80% of the CDL was calculated using (i) linear and (ii) non-linear models.
Results: The analysis of 120 HRCT data sets showed significantly different model-dependent CDL. Significant differences between the CC of 70% assessed from linear and non-linear models (mean difference: 2.5 mm, p < 0.001) and the CC of 80% assessed from linear and non-linear models (mean difference: 1.5 mm, p < 0.001) were found. In up to 25% of the patients none of the existing flexible lateral wall electrode arrays fit into this range. In 59 cases (49,2%) the models did not agree on the suitable electrode arrays.
Conclusions: The CC varies depending on the underlying CDL approximation, which critically influences electrode array choice. Based on the literature, we hypothesize that the non-linear method systematically overestimates the CC and may lead to rather too short electrode array choices. Future studies need to assess the accuracy of the individual mathematical models.
Keywords: Angular insertion depth prediction; Insertion angle.
© 2023. The Author(s).
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- Disorders, N.I.O.D.A.O.C. NIH Publication No. 00–4798. 2021
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