{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T17:41:25Z","timestamp":1792690885479,"version":"4.5.0"},"reference-count":36,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T00:00:00Z","timestamp":1692921600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Laryngoscope Investig Oto"],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>To investigate whether machine learning (ML)\u2010based algorithms, namely logistic regression (LR), random forest (RF), k\u2010nearest neighbor (k\u2010NN), and gradient\u2010boosting decision tree (GBDT), utilizing early post\u2010onset parameters can predict facial synkinesis resulting from Bell's palsy or Ramsay Hunt syndrome more accurately than the conventional statistics\u2010based LR.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This retrospective study included 362 patients who presented to a facial palsy outpatient clinic. Median follow\u2010up of synkinesis\u2010positive and \u2010negative patients was 388 (range, 177\u20131922) and 198 (range, 190\u20133021) days, respectively. Electrophysiological examinations were performed, and the rate of synkinesis in Bell's palsy and Ramsay Hunt syndrome was evaluated. Sensitivity and specificity were assessed using statistics\u2010based LR; and electroneurography (ENoG) value, the difference in the nerve excitability test (NET), and scores of the subjective Yanagihara scaling system were evaluated using early post\u2010onset parameters with ML\u2010based LR, RF, <jats:italic>k<\/jats:italic>\u2010NN, and GBDT.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Synkinesis rate in Bell's palsy and Ramsay Hunt syndrome was 20.2% (53\/262) and 40.0% (40\/100), respectively. Sensitivity and specificity obtained with statistics\u2010based LR were 0.796 and 0.806, respectively, and the area under the receiver operating characteristic curve (AUC) was 0.87. AUCs measured using ML\u2010based LR of \u201cENoG,\u201d \u201cdifference in NET,\u201d \u201cYanagihara,\u201d and all three components (\u201call\u201d) were 0.910, 0.834, 0.711, and 0.901, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>ML\u2010based LR model shows potential in predicting facial synkinesis probability resulting from Bell's palsy or Ramsay Hunt syndrome and has comparable reliability to the conventional statistics\u2010based LR.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>3.<\/jats:p><\/jats:sec>","DOI":"10.1002\/lio2.1145","type":"journal-article","created":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T12:14:42Z","timestamp":1692965682000},"page":"1189-1195","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Predicting synkinesis caused by Bell's palsy or Ramsay Hunt syndrome using machine learning\u2010based logistic regression"],"prefix":"10.1002","volume":"8","author":[{"given":"Megumi","family":"Kishimoto\u2010Urata","sequence":"first","affiliation":[{"name":"Department of Otolaryngology, Graduate School of Medicine The University of Tokyo  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5947-6842","authenticated-orcid":false,"given":"Shinji","family":"Urata","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology, Graduate School of Medicine The University of Tokyo  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9056-9900","authenticated-orcid":false,"given":"Hironobu","family":"Nishijima","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology, Graduate School of Medicine The University of Tokyo  Tokyo 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