Development of a Machine Learning Prediction Model for the Detection of Meniere's Disease from Cerumen Chemical Profiles

开发机器学习预测模型，用于根据耵聍化学特征检测梅尼埃病

• 批准号: 10510948
• 负责人: RABI A MUSAH
• 金额: $ 23万
• 依托单位: STATE UNIVERSITY OF NEW YORK AT ALBANY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10510948
• 关键词: Anecdotes; Appearance; Characteristics; Chemicals; Chronic; Clinical; Collection; Complex Mixtures; Consumption; Data; Detection; Development; Diagnosis; Diagnostic; Diagnostic tests; Discrimination; Disease; Ear; Earwax; Endolymph; Etiology; Feeling; Hand; Health; Hearing Tests; High Pressure Liquid Chromatography; Individual; Knowledge; Labyrinth; Lipids; Low Frequency Deafness; Machine Learning; Magnetic Resonance Imaging; Mass Fragmentography; Mass Spectrum Analysis; Meniere' s Disease; Methods; Molecular; Nausea; Neurologic; Nuclear Magnetic Resonance; Pathogenesis; Patients; Preparation; Process; Recurrence; Reporter; Reporting; Research; Resolution; Sampling; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Statistical Data Interpretation; Symptoms; Techniques; Time; Tinnitus; Vertigo; Vomiting; Work; accurate diagnosis; balance testing; base; cost; cost effective; disease diagnosis; experience; feature selection; infrared spectroscopy; machine learning prediction; nervous system disorder; pressure; radiological imaging; random forest; rapid diagnosis; rapid technique; two-dimensional

ABSTRACT/PROJECT SUMMARY Ménière’s disease is a chronic, incurable vestibular disorder that produces a recurring set of symptoms as a result of abnormally large amounts of endolymph in the inner ear. Manifestations of the disease include recurrent episodes of vertigo, tinnitus, imbalance, nausea and/or vomiting, a feeling of fullness or pressure in the ear, and fluctuating, progressive low-frequency hearing loss. Diagnosis is difficult because other neurological conditions present some of the same symptoms. Thus, Ménière’s disease diagnosis, which is challenging, imprecise, and time consuming, involves the painstaking process of excluding other diseases with overlapping symptoms. Because it has no known chemical or radiographic markers, diagnosis is based on the observation of a clinical compendium of symptoms, and misdiagnosis is fairly common. If chemical markers of Ménière’s and other relevant neurological disorders could be determined, more rapid and accurate diagnosis could be achieved based on assessment of the presence (or absence) of these relevant compounds. It is hypothesized here that the chemical profile of cerumen can serve as a reporter of the presence of Ménière’s disease and other neurological disorders with overlapping symptoms, and that knowledge of these differential profiles can be leveraged to accurately and rapidly reveal the presence of Ménière’s disease. This hypothesis will be investigated through pursuit of the following specific aims: Specific Aim I: Collection and determination of the mass spectral chemical signatures of cerumen from healthy donors, Ménière’s disease patients, and patients diagnosed with other neurotological disorders with overlapping symptoms. Specific Aim II: Development of machine learning prediction models that enable accurate determination of the presence of Ménière’s disease and/or other neurotological disorders from cerumen chemical profiles, and reveal the presence of the subset of compounds that are important for the ability to distinguish Ménière’s disease samples from others. Specific Aim III: Structural characterization of compounds revealed by the machine learning prediction model(s) developed in Specific Aim II, to be associated with Ménière’s disease. The results of this work will reveal whether there is a correlation between the lipid profile of earwax and the presence of particular disease states. Structural information will be acquired on the molecules that are responsible for the differences in healthy and Ménière’s disease patients. The information revealed would provide the opportunity for development of a potential non-invasive method for the rapid diagnosis of Ménière’s disease.

摘要/项目摘要 梅尼埃病是一种慢性、无法治愈的前庭疾病，会产生一系列反复出现的症状： 内耳异常大量内淋巴导致的疾病表现包括： 反复发作的眩晕、耳鸣、失衡、恶心和/或呕吐、饱腹感或压力感 由于其他原因，波动性进行性低频听力损失的诊断很困难。 神经系统疾病会出现一些相同的症状，因此，梅尼埃病的诊断是： 具有挑战性、不精确且耗时，涉及排除其他疾病的艰苦过程 由于没有已知的化学或放射学标记，因此诊断有重叠的症状。 根据临床症状概要的观察，如果化学的话，误诊是相当常见的。 可以更快、更准确地确定梅尼埃病和其他相关神经系统疾病的标志物 根据对这些相关因素的存在（或不存在）的评估，可以实现准确的诊断。 这里追求陶瓷的化学特征可以作为该化合物的报告者。 存在梅尼埃病和其他具有重叠症状的神经系统疾病，并且 可以利用这些差异概况的知识来准确、快速地揭示 梅尼埃病将通过追求以下具体目标进行研究： 具体目标 I：收集和测定陶瓷的质谱化学特征 健康捐献者、梅尼埃病患者以及诊断患有其他神经系统疾病的患者 重叠的症状。 具体目标二：开发能够准确判断的机器学习预测模型 根据耵聍化学特征判断是否存在梅尼埃病和/或其他神经系统疾病， 并揭示对于区分能力很重要的化合物子集的存在 梅尼埃病样本来自其他人。 具体目标 III：机器学习预测揭示的化合物的结构表征 在 Specific Aim II 中开发的与梅尼埃病相关的模型。 这项工作的结果将揭示耳垢的脂质谱与耳垢之间是否存在相关性。 特定疾病状态的存在将获得分子的结构信息。 造成健康和梅尼埃病患者差异的原因是所揭示的信息。 为开发一种潜在的非侵入性方法来快速诊断提供了机会 梅尼埃病。