Improving an EEG-based neurodiagnostic software platform to detect Alzheimer's Disease in MCI patients

项目总结 阿尔茨海默病(AD)是一种进行性的神经退行性疾病，是 痴呆症。在美国，估计有620万65岁以上的人患有阿尔茨海默病，其中72% 他们已经超过75岁了。考虑到该国人口老龄化，这一数字预计将增加两倍以上 到2050年，美国每年的相关医疗成本将达到6000亿美元。早期诊断是 对AD治疗至关重要，因为它让临床医生有更多的时间寻找和启动治疗途径，这 延缓疾病进展，保持智力。新的研究表明，生物标记物可以 在症状出现前几年帮助诊断AD。尽管最近技术进步，但许多工具和 测量生物标记物的技术是侵入性的、昂贵的、不够敏感或足够具体的，特别是 在早期阶段检测疾病时，限制了它们的可用性。 当与先进的机器学习技术相结合时，脑电(EEG)已经被展示出来 以解决与AD生物标记物相关的许多现有问题。在Spark Neuro，我们的目标是解锁 通过一种新的软件平台实现脑电的潜力。将脑电与机器学习能力相结合， 我们的模型更好地评估认知健康和神经退化，有助于AD的诊断。斯帕克的 神经分析平台将是一种标准化、客观、非侵入性、经济高效的诊断工具，能够 在整个疾病连续体中对认知障碍进行高度敏感和特异的检测。我们的平台 将极大地扩展AD筛查计划，并提供神经学见解以帮助诊断和跟踪 疾病的进展。 在拟议的第一阶段研究期间，我们将与梅奥诊所合作，扩大我们目前的 评估和区分疾病早期轻度认知障碍阶段的患者的算法，以及 为临床医生提供高度有用和有用的报告。首先，我们将通过结合EEG来优化算法 数据收集自梅奥诊所的患者。接下来，我们将重点改善两种脑电数据的用户体验 收购和临床报告。我们将提高最终用户满意度，并优化技术以适应 当前的临床工作流程。参与的梅奥诊所脑电技术人员将提供反馈。一旦优化， Spark的方法将构成第一个基于办公室EEG的神经诊断工具，专门用于诊断 和追踪广告。我们的非侵入性解决方案有可能加速AD筛查计划，检测 病理性AD在早期阶段，并提供个性化的疾病进展洞察。

PROJECT SUMMARY Alzheimer’s disease (AD) is a progressive, neurodegenerative condition and the most common cause of dementia. In the United States, an estimated 6.2 million people over the age of 65 are living with AD, 72% of whom are over 75 years old. Given the country’s aging population, this number is expected to more than triple by 2050, costing the United States an annual $600 billion in associated healthcare costs. Early diagnosis is crucial to AD treatment because it allows clinicians more time to find and initiate treatment pathways, which decreases disease progression and preserves mental capacity. New research suggests that biomarkers can help diagnose AD years before symptoms appear. Despite recent technological advancements, many tools and technologies that measure biomarkers are invasive, expensive, and not sensitive or specific enough, particularly when detecting the disease at earlier stages, limiting their usability. When combined with advanced machine learning techniques, electroencephalography (EEG) has been shown to address many of the existing issues related to AD biomarkers. At SPARK Neuro, we aim to unlock the full potential of EEG through a novel software platform. Combining EEG with the capabilities of machine learning, our model better assesses cognitive health and neurodegeneration, aiding the diagnosis of AD. SPARK’s neuroanalytic platform will be a standardized, objective, non-invasive, cost-effective diagnostic tool capable of highly sensitive and specific detection of cognitive impairment across the entire disease continuum. Our platform would vastly expand AD screening initiatives and provide neurological insights to aid in the diagnosis and tracking of disease progression. During the proposed Phase I research, we will work in collaboration with Mayo Clinic to extend our current algorithm to assess and differentiate patients in the earlier, mild cognitive impairment stage of the disease, and provide highly useful and usable reports to clinicians. First, we will optimize the algorithm by incorporating EEG data collected from Mayo Clinic patients. Next, we will focus on improving the user experience of both EEG data acquisition and clinical reporting. We will enhance end-user satisfaction and optimize the technology to fit within current clinical workflows. Participating Mayo Clinic EEG technicians will provide feedback. Once optimized, SPARK’s approach will constitute the first in-office EEG-based neurodiagnostic tool specifically for diagnosing and tracking AD. Our non-invasive solution has the potential to accelerate AD screening programs, detect pathological AD at earlier stages, and provide individualized disease progression insights.