Machine Learning to Predict Adult Cochlear Implant Candidacy

Abstract

Abstract Purpose of Review The purpose of this review is to summarize candidacy criteria and commonly used referral guidelines for adult cochlear implant (CI) patients. This review describes how machine learning can be used to predict CI candidacy and the potential impact of an automated referral guideline. Recent Findings Less than 2% of eligible adults are receiving CIs under traditional and expanded candidacy criteria. Lack of understanding of referral criteria, both among providers and patients, significantly contributes to the underutilization of CIs. Recently, a novel machine learning-based CI referral algorithm has been developed that shows high sensitivity, specificity, and accuracy in predicting CI candidacy among adults. Summary An automated, machine learning-based referral guideline can mitigate the lack of clarity regarding when to refer a patient and help bridge the large gap in CI care delivery that currently exists. Future research needs to externally validate such an algorithm and evaluate its uptake in routine clinical settings.