Prediction of Cochlear Implant Fitting by Machine Learning Techniques.

Journal: Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology

PMID: 38769101

Abstract

OBJECTIVE: This study aimed to evaluate the differences in electrically evoked compound action potential (ECAP) thresholds and postoperative mapping current (T) levels between electrode types after cochlear implantation, the correlation between ECAP thresholds and T levels, and the performance of machine learning techniques in predicting postoperative T levels.

Authors

Hajime Koyama

Department of Otorhinolaryngology and Head and Neck Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Akinori Kashio
Tatsuya Yamasoba

Department of Otolaryngology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.

Keywords

Child Child, Preschool Cochlear Implantation Cochlear Implants Evoked Potentials, Auditory Female Humans Infant Machine Learning Male Prosthesis Fitting Retrospective Studies

External Resources

View on PubMed Access via DOI PubMed (38769101)

Prediction of Cochlear Implant Fitting by Machine Learning Techniques.

Abstract

Authors

Keywords

External Resources

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