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 Ménière病是一种长期无法治愈的前庭障碍，会产生一组反复出现的症状 在内耳中绝对大量的内淋巴的结果。疾病的表现包括 眩晕，耳鸣，失衡，恶心和/或呕吐的复发情节，充满或压力的感觉 在耳朵中，渐进的低频听力损失。诊断很困难，因为其他 神经疾病表现出一些相同的症状。那是梅尼尔病的诊断，那就是 具有挑战性，暗示和耗时，涉及排除其他疾病的艰苦过程 带有重叠的符号。因为它没有已知的化学或放射学标记，所以诊断是基于的 关于观察症状的临床汇编，并且诊断很常见。如果化学 可以确定，更快，更快，并且 可以根据评估这些相关的存在（或不存在）来实现准确的诊断 化合物。在这里假设Cerumen的化学轮廓可以作为记者 梅尼氏病和其他具有重叠症状的神经系统疾病的存在，并且 可以利用这些差异概况的知识，以准确，迅速地揭示 梅尼氏病。该假设将通过追求以下特定目的进行调查： 特定目的I：收集和确定Cerumen的质谱化学特征 健康的供体，梅尼尔病患者以及被诊断为其他神经学疾病患者 重叠的符号。 特定目的II：开发机器学习预测模型，以实现准确的确定 梅尼氏病和/或其他神经学疾病的存在， 并揭示对区分能力很重要的化合物子集的存在 梅尼尔病的样本。 特定目标III：机器学习预测揭示的化合物的结构表征 在特定目标II中开发的模型与Ménière病有关。 这项工作的结果将揭示早期沃克斯的脂质轮廓和 特定疾病状态的存在。结构信息将在分子上获取 负责健康和梅尼尔病患者的差异。揭示的信息将 提供了开发潜在的非侵入性方法的机会，以快速诊断 梅尼氏病。