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Published on: January 17, 2025
Using Machine Learning and Multifaceted Preoperative Measures to Predict Adult Cochlear Implant Outcomes: A
Ankita Patro1,2, Patrick J Lawrence3,2, Terrin N Tamati1
1Department of Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Objectives:
To use machine learning and a battery of measures for preoperative prediction of speech recognition and quality of life (QOL) outcomes after cochlear implant (CI) surgery.
Design:
Demographic, audiologic, cognitive-linguistic, and QOL predictors were collected from 30 postlingually deaf adults before CI surgery. K-means clustering separated patients into groups. Reliable change index scores were computed for speech recognition and QOL from pre-CI to 6 months post-CI, and group differences were determined.
Results:
Clustering yielded three groups with differences in reliable change index for sentence recognition. One group demonstrated low baseline sentence recognition and only small improvements post-CI, suggesting a group "at risk" for limited benefits. This group showed lower pre-CI scores on verbal learning and memory and lack of musical training.
Conclusions:
Preoperative assessments can prognosticate CI recipients' postoperative performance and identify individuals at risk for experiencing poor sentence recognition outcomes, which may help guide counseling and rehabilitation.
