{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T19:10:46Z","timestamp":1783451446051,"version":"3.55.0"},"reference-count":65,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T00:00:00Z","timestamp":1666742400000},"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":[[2023,9]]},"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>This retrospective study aimed to evaluate the performance of machine learning techniques in predicting air\u2010bone gap after tympanoplasty compared with conventional scoring models and to identify the influential factors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We reviewed the charts of 105 patients (114 ears) with chronic otitis media who underwent tympanoplasty. Two numerical scoring systems (middle ear risk index [MERI] and ossiculoplasty outcome parameter staging [OOPS]) and three algorithms (random forest [RF], support vector machine [SVM], and k nearest neighbor [kNN]) were created. Experimental variables included age, preoperative air\u2010bone gap, soft\u2010tissue density lesion in the tympanic cavity in CT, otorrhea, surgical history, ossicular bone problems in CT, tympanic perforation location, perforation type (central or marginal), grafting material, smoking history, endoscopy use, and the operator whose experience was 20\u2009years or longer, or shorter. Binary classification, postoperative air\u2010bone gap \u226415 or &gt;15\u2009dB, and multiclass classification, classification into seven categories by 10\u2009dB, were performed, and the percentages of correct prediction were calculated. The importance of features in the RF model was calculated to identify influential factors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The percentages of correct prediction in binary classification were 62.3%, 72.8%, 81.5%, 81.5%, and 81.5% in MERI, OOPS, RF, SVM, and kNN, respectively, and those in multiclass classification were 29.8%, 21.9%, 63.1%, 44.7%, and 50% in the same order. The RF model suggested larger preoperative air\u2010bone gap, and older age could make the postoperative air\u2010bone gap larger.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The machine learning techniques, especially the RF model, are promising methods for precise postoperative air\u2010bone gap prediction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>4 <jats:italic>Laryngoscope<\/jats:italic>, 133:2371\u20132378, 2023<\/jats:p><\/jats:sec>","DOI":"10.1002\/lary.30457","type":"journal-article","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T11:43:56Z","timestamp":1666784636000},"page":"2371-2378","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Application of Machine Learning to Predict Hearing Outcomes of Tympanoplasty"],"prefix":"10.1002","volume":"133","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3190-3320","authenticated-orcid":false,"given":"Hajime","family":"Koyama","sequence":"first","affiliation":[{"name":"Department of Otorhinolaryngology and Head and Neck Surgery Graduate School of Medicine, The University of Tokyo  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akinori","family":"Kashio","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology and Head and Neck Surgery Graduate School of Medicine, The University of Tokyo  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9346-6456","authenticated-orcid":false,"given":"Tsukasa","family":"Uranaka","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology and Head and Neck Surgery Graduate School of Medicine, The University of Tokyo  Tokyo 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