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

摘要/项目总结 梅尼埃病是一种慢性、不可治愈的前庭疾病， 内耳内淋巴异常大量的结果。疾病的表现包括 反复发作的眩晕、耳鸣、失衡、恶心和/或呕吐、饱胀感或压迫感 以及波动性、进行性低频听力损失。诊断很困难，因为其他 神经系统疾病也会出现一些相同的症状。因此，梅尼埃病的诊断， 具有挑战性，不精确，耗时，涉及排除其他疾病的艰苦过程 症状重叠因为它没有已知的化学或放射学标记，诊断是基于 根据对临床症状的观察，误诊是相当常见的。如果化学 梅尼埃氏病和其他相关神经系统疾病的标志物可以更快， 准确的诊断可以根据这些相关的存在（或不存在）的评估来实现。 化合物.在此假设，耳垢的化学特征可以作为耳垢的报告者。 存在梅尼埃病和其他神经系统疾病，症状重叠， 这些差异分布的知识可以用来准确和快速地揭示 梅尼埃病将通过追求以下具体目标对这一假设进行调查： 具体目标I：收集和测定来自人的耳垢的质谱化学特征， 健康供体、梅尼埃病患者和诊断为其他神经耳病学疾病的患者， 重叠症状 具体目标二：开发能够准确确定的机器学习预测模型 根据耳垢化学特征确定是否存在梅尼埃病和/或其他神经耳科疾病， 并揭示了化合物子集的存在，这些化合物对于区分 其他人的梅尼埃病样本。 具体目标III：通过机器学习预测揭示化合物的结构表征 在Specific Aim II中开发的模型与梅尼埃病相关。 这项工作的结果将揭示是否有一个相关性之间的脂质分布的耳垢和 特定疾病状态的存在。将获得分子的结构信息， 造成健康人和梅尼埃病患者的差异。透露的信息将 为开发一种潜在的非侵入性快速诊断方法提供了机会。 梅尼埃病

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.