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监督机器学习算法在梅尼埃病患者耳石器功能评估中的应用:利用主观视觉垂直和眼-前庭肌源性电位。

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.

机构信息

Department of Otolaryngology-Head and Neck Surgery, Northwestern Medicine, Chicago, IL, USA.

Department of Otolaryngology-Head and Neck Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.

出版信息

Acta Otolaryngol. 2023 Apr;143(4):262-273. doi: 10.1080/00016489.2023.2190163. Epub 2023 Apr 17.

DOI:10.1080/00016489.2023.2190163
PMID:37067348
Abstract

BACKGROUND

Research on the otolith organs remains inconclusive.

OBJECTIVES

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.

METHODS

A prospective study with ninety subjects.

RESULTS

(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.

SIGNIFICANCE

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.

摘要

背景

耳石器官的研究仍未有定论。

目的

本研究旨在通过三种方式进一步阐明梅尼埃病(MD)患者的椭圆囊功能:(1)我们旨在阐明主观视觉垂直(SVV)和眼动前庭诱发肌源性电位(o-VEMP)测试在测量哪个椭圆囊亚系方面的作用。(2)我们试图通过识别急性和慢性 MD 患者的 SVV 和 o-VEMP 结果之间的关系差异,来描述 MD 的急性和慢性状态。(3)我们尝试找到一种机器学习算法,能够使用 SVV 和 o-VEMP 预测急性 MD 与慢性 MD。

方法

一项前瞻性研究,共纳入 90 名受试者。

结果

(1)急性 MD 患者的 SVV 和 o-VEMP 测试呈中度线性关系,表明这两种测试都测量了不同的椭圆囊亚系。(2)回归分析在两种患者群体之间存在统计学差异,表明慢性 MD 患者的 SVV 结果得到了正常化。(3)逻辑回归和朴素贝叶斯算法被发现可以准确预测急性 MD 和慢性 MD。

意义

对诊断测试测量什么的更好理解将导致未来对 MD 有更好的分类系统和更有针对性的治疗选择。

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