Application of supervised machine learning algorithms for the evaluation of utricular function on patients with Meniere's disease: utilizing subjective visual vertical and ocular-vestibular-evoked myogenic potentials.
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| Title: | Application of supervised machine learning algorithms for the evaluation of utricular function on patients with Meniere's disease: utilizing subjective visual vertical and ocular-vestibular-evoked myogenic potentials. |
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| Authors: | Bragg, Phillip G. (AUTHOR), Norton, Benjamin M. (AUTHOR), Petrak, Michelle R. (AUTHOR), Weiss, Allyson D. (AUTHOR), Kandl, Lindsay M. (AUTHOR), Corrigan, Megan L. (AUTHOR), Bahner, Cammy L. (AUTHOR), Matsuoka, Akihiro J. (AUTHOR) |
| Source: | Acta Oto-Laryngologica. Apr2023, Vol. 143 Issue 4, p262-273. 12p. |
| Subjects: | Vestibular apparatus physiology, Auditory evoked response, Evoked potentials (Electrophysiology), Machine learning, Ménière's disease, Research funding, Logistic regression analysis, Visual evoked response, Algorithms, Longitudinal method, Probability theory, Neurologic examination |
| Abstract (English): | Research on the otolith organs remains inconclusive. This study seeks to further elucidate utricular function in patients with Meniere's disease (MD) in three ways: (1) We aimed to disambiguate the role of the Subjective Visual Vertical (SVV) and Ocular Vestibular Evoked Myogenic Potential (o-VEMP) tests regarding which utricular subsystem each is measuring. (2) We sought to characterize the acute and chronic state of MD by identifying differences in the relationship of SVV and o-VEMP results across patients with acute and chronic MD. (3) We attempted to find a machine-learning algorithm that could predict acute versus chronic MD using SVV and o-VEMP. A prospective study with ninety subjects. (1) SVV and o-VEMP tests were found to have a moderate linear relationship in patients with acute MD, suggesting each test measures a different utricular subsystem. (2) Regression analyses statistically differed across the two patient populations, suggesting that SVV results were normalized in chronic MD patients. (3) Logistic regression and Naïve Bayes algorithms were found to predict acute and chronic MD accurately. A better understanding of what diagnostic tests measure will lead to a better classification system for MD and more targeted treatment options in the future. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): | 背景：对耳石器官的研究且尚无定论。 目的：本研究旨在进一步阐明梅尼埃病(MD)患者的椭圆囊功能。以三种方式来达到此目的：(1)我们旨在澄清主观视觉垂直(SVV)测试和眼前庭诱发肌源性电位 (o-VEMP) 测试对受测量的每个椭圆囊子系统的作用。 (2) 我们试图通过鉴别急性和慢性 MD 患者的 SVV 和 o-VEMP 结果之间关系的差异来表征 MD 的急性和慢性状态。 (3) 我们试图找到一种机器学习算法, 它可以利用 SVV 和 o-VEMP来预测急性与慢性 MD 。 方法：一项有 90 名受试者参与的前瞻性研究。 结果：(1)发现 SVV 和 o-VEMP 测试在急性 MD患者中具有中度线性关系, 说明每项测试测量不同的椭圆囊子系统。 (2) 回归分析在两种患者群体中存在统计学差异, 表明 慢性MD患者的SVV 结果趋于正常化。 (3) 发现逻辑回归和Naïve Bayes算法可以准确预测急性和慢性 MD。 意义：更好地了解诊断测试测量的内容将导致更好的 MD 分类系统和未来更有针对性的治疗选择。 [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Research on the otolith organs remains inconclusive. This study seeks to further elucidate utricular function in patients with Meniere's disease (MD) in three ways: (1) We aimed to disambiguate the role of the Subjective Visual Vertical (SVV) and Ocular Vestibular Evoked Myogenic Potential (o-VEMP) tests regarding which utricular subsystem each is measuring. (2) We sought to characterize the acute and chronic state of MD by identifying differences in the relationship of SVV and o-VEMP results across patients with acute and chronic MD. (3) We attempted to find a machine-learning algorithm that could predict acute versus chronic MD using SVV and o-VEMP. A prospective study with ninety subjects. (1) SVV and o-VEMP tests were found to have a moderate linear relationship in patients with acute MD, suggesting each test measures a different utricular subsystem. (2) Regression analyses statistically differed across the two patient populations, suggesting that SVV results were normalized in chronic MD patients. (3) Logistic regression and Naïve Bayes algorithms were found to predict acute and chronic MD accurately. A better understanding of what diagnostic tests measure will lead to a better classification system for MD and more targeted treatment options in the future. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00016489 |
| DOI: | 10.1080/00016489.2023.2190163 |
