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Updated: Jun 16, 2025

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

Published on: March 24, 2023

A Machine Learning Model to Predict Postoperative Speech Recognition Outcomes in Cochlear Implant Recipients:

Alexey Demyanchuk^1,2, Eugen Kludt^1,2, Thomas Lenarz^1,2

¹Department of Otorhinolaryngology, Hannover Medical School, Carl-Neuberg-Straße 1, 30625 Hannover, Germany.

Journal of Clinical Medicine

|June 13, 2025

Summary

A new machine learning model accurately predicts cochlear implant (CI) speech outcomes, matching expert audiologist predictions. This tool could aid clinical decisions for patients with sensorineural hearing loss.

Keywords:

clinical decision-making cochlear implantation hearing loss machine learning predictive modeling

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Last Updated: Jun 16, 2025

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Performing Intracochlear Electrocochleography During Cochlear Implantation

Performing Intracochlear Electrocochleography During Cochlear Implantation

Published on: March 8, 2022

Area of Science:

Otolaryngology
Biomedical Engineering
Data Science

Background:

Cochlear implantation (CI) improves hearing but outcomes vary.
Predicting CI success preoperatively is difficult.
Large datasets are needed to develop predictive models.

Purpose of the Study:

Develop and validate a machine learning model for predicting postoperative speech recognition after CI.
Compare the model's predictive accuracy against expert clinical predictions.

Main Methods:

Retrospective analysis of 2571 adult patients with postlingual hearing loss.
Decision tree regression model trained on preoperative variables.
Model validated on random and chronological future cohorts, and compared to audiologist predictions.

Main Results:

The model achieved comparable accuracy to expert predictions (MAE 17.3-17.8%).
Robust predictive performance demonstrated across different validation sets.
Machine learning model showed similar effectiveness to experienced audiologists.

Conclusions:

Machine learning offers a reliable method for predicting CI speech outcomes.
The developed model shows potential for clinical decision support.
Further external validation and prospective studies are warranted.