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

This study demonstrates how ML models can offer superior performance in CI speech perception outcomes prediction modeling compared with current gold standard (Lazard-linear or logistic regression). ML offers novel insights capable of capturing nonlinear complex relationships and can identify novel sub-phenotypes at the extremes of CI performance using preoperative clinical variables alone. This is the first study to our knowledge to offer any type of meaningful preoperative stratification for CI speech perception outcomes and may have significant implications that need to be carefully explored when it comes to patient counseling, auditory rehabilitation, and future CI clinical trials. While prospective validation is a necessary next step and performance is still limited based on current traditional CI variables, these results highlight the potential of artificial intelligence (AI) in CI care, the critical need to integrate novel variables that better account for CI performance, and the need for improved data collaboration and standardized registries moving forward.