Machine Learning for Vestibular Schwannoma Diagnosis Using Audiometrie Data Alone. Issue 5 (June 2022)

• Record Type: Journal Article
• Authors: Carey, Grace E.; Jacobson, Clare E.; Warburton, Alyssa N.; Biddle, Elliot; Mannarelli, Greg; Wilson, Michael; Stucken, Emily Z.
• Is Part Of: Otology & neurotology. Volume 43:Issue 5(2022)
• Page Start: e530
• Page End: e534
• Publication Date: 2022-06
• Subjects: Acoustic neuroma -- Artificial neural network -- Asymmetric hearing loss -- Audiogram -- Audiometric screening -- Cerebellopontine angle tumor -- Machine learning -- Magnetic resonance imaging -- Vestibular schwannoma; Otology -- Periodicals; Ear -- Diseases -- Periodicals; Skull base -- Surgery -- Periodicals; 617.8005
• DOI: 10.1097/MAO.0000000000003539
• Languages: English
• ISSNs: 1531-7129

Abstract

Abstract : Objective: 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. Study Design: Retrospective case-control study. Setting: Tertiary referral center. Patients: Seven hundred sixty seven adult patients with confirmed vestibular schwannoma and a pretreatment audiogram on file and 2000 randomly selected adult controls with audiograms. Intervention(s): Audiometric data were analyzed using machine learning algorithms and standard rule-based criteria for defining asymmetric hearing loss. Main Outcome Measures: 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. Results: 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. Conclusions: 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.