{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T10:59:58Z","timestamp":1776423598563,"version":"3.51.2"},"reference-count":68,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T00:00:00Z","timestamp":1689292800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Laryngoscope"],"published-print":{"date-parts":[[2024,2]]},"abstract":"<jats:sec><jats:title>Objectives<\/jats:title><jats:p>The aim of the study was to train and test supervised machine\u2010learning classifiers to predict acoustic hearing preservation after CI using preoperative clinical data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Design<\/jats:title><jats:p>Retrospective predictive modeling study of prospectively collected single\u2010institution CI dataset.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>One hundred and seventy\u2010five patients from a REDCap database including 761 patients &gt;18\u2009years who underwent CI and had audiometric testing preoperatively and one month after surgery were included. The primary outcome variable was the lowest quartile change in acoustic hearing at one month after CI using various formulae (standard pure tone average, SPTA; low\u2010frequency PTA, LFPTA). Analysis involved applying multivariate logistic regression to detect statistical associations and training and testing supervised learning classifiers. Classifier performance was assessed with numerous metrics including area under the receiver operating characteristic curve (AUC) and Matthews correlation coefficient (MCC).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Lowest quartile change (indicating hearing preservation) in SPTA was positively associated with a history of meningitis, preoperative LFPTA, and preoperative SPTA. Lowest quartile change in SPTA was negatively associated with sudden hearing loss, noise exposure, aural fullness, and abnormal anatomy. Lowest quartile change in LFPTA was positively associated with preoperative LFPTA. Lowest quartile change in LFPTA was negatively associated with tobacco use. Random forest demonstrated the highest mean classification performance on the validation dataset when predicting each of the outcome variables.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Machine learning demonstrated utility for predicting preservation of residual acoustic hearing in patients undergoing CI surgery, and the detected associations facilitated the interpretation of our machine\u2010learning models. The models and statistical associations together may be used to facilitate improvements in shared clinical decision\u2010making and patient outcomes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>3 <jats:italic>Laryngoscope<\/jats:italic>, 134:926\u2013936, 2024<\/jats:p><\/jats:sec>","DOI":"10.1002\/lary.30894","type":"journal-article","created":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T11:44:15Z","timestamp":1689335055000},"page":"926-936","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Predicting Acoustic Hearing Preservation Following Cochlear Implant Surgery Using Machine Learning"],"prefix":"10.1002","volume":"134","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5506-5483","authenticated-orcid":false,"given":"Daniel M","family":"Zeitler","sequence":"first","affiliation":[{"name":"Neuroscience Institute Virginia Mason Franciscan Health  Seattle Washington 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