Anatomical Considerations for Achieving Optimized Outcomes in Individualized Cochlear Implantation
Machine learning models can predict insertion depths needed for optimal insertion angles, with performance improving by including cochlear dimensions in the models. A simple linear regression using just the insertion depth explained 88% of variability, whereas adding cochlear length or diameter and width further improved predictions up to 94%.
Authors
- Daniel Schurzig (ORCID: https://orcid.org/0000-0003-2197-1685)
- Rolf Salcher
- Thomas Lenarz (ORCID: https://orcid.org/0000-0002-9307-5989)
- Niels Rudnik (ORCID: https://orcid.org/0009-0006-5524-4928)
- Emilio Avallone (ORCID: https://orcid.org/0000-0002-5246-5307)
- Max Timm (ORCID: https://orcid.org/0000-0002-6610-1950)
- Malena Timm
Institutions
- Medizinische Hochschule Hannover (DE)
Publication Details
- Journal
- Otology & Neurotology
- Published
- 2025-05-15
- DOI
- https://doi.org/10.1097/mao.0000000000004520
- Citations
- 2
- Primary Topic
- Hearing Loss and Rehabilitation
- Type
- article
- Field-Weighted Citation Impact
- 2.86