Machine Learning Feasibility in Cochlear Implant Speech Perception Outcomes-Moving Beyond Single Biomarkers for Cochlear Implant Performance Prediction.

ABSTRACT:

Objectives

Machine learning (ML) is an emerging discipline centered around complex pattern matching and large data-based prediction modeling and can improve precision medicine healthcare. Cochlear implants (CI) are highly effective, however, outcomes vary widely, and accurately predicting speech perception performance outcomes between patients remains a challenge. This study aims to evaluate the ability of ML to predict speech perception performance among CI recipients at 6-month post-implantation using only preoperative variables on one of the largest CI datasets to date, with an emphasis placed on identification of poor performers.

Design

All patients enrolled in the national CI outcome tracking database, HERMES, and the institutional CI registry. Data were split 90/10 tra

SUBMITTER: Shew MA 

PROVIDER: S-EPMC12352561 | biostudies-literature | 2025 Sep-Oct 01

REPOSITORIES: biostudies-literature