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• W4281552362 abstract "The aim of this study is to compare machine learning algorithms and established rule-based evaluations in screening audiograms for the purpose of diagnosing vestibular schwannomas. A secondary aim is to assess the performance of rule-based evaluations for predicting vestibular schwannomas using the largest dataset in the literature.Retrospective case-control study.Tertiary referral center.Seven hundred sixty seven adult patients with confirmed vestibular schwannoma and a pretreatment audiogram on file and 2000 randomly selected adult controls with audiograms.Audiometric data were analyzed using machine learning algorithms and standard rule-based criteria for defining asymmetric hearing loss.The primary outcome is the ability to identify patients with vestibular schwannomas based on audiometric data alone, using machine learning algorithms and rule-based formulas. The secondary outcome is the application of conventional rule-based formulas to a larger dataset using advanced computational techniques.The machine learning algorithms had mildly improved specificity in some fields compared with rule-based evaluations and had similar sensitivity to previous rule-based evaluations in diagnosis of vestibular schwannomas.Machine learning algorithms perform similarly to rule-based evaluations in identifying patients with vestibular schwannomas based on audiometric data alone. Performance of established rule-based formulas was consistent with earlier performance metrics, when analyzed using a large dataset." @default.
• W4281552362 created "2022-05-27" @default.
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• W4281552362 date "2022-06-01" @default.
• W4281552362 modified "2023-09-26" @default.
• W4281552362 title "Machine Learning for Vestibular Schwannoma Diagnosis Using Audiometrie Data Alone" @default.
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• W4281552362 doi "https://doi.org/10.1097/mao.0000000000003539" @default.
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