An integrated electrical impedance myography platform for neuromuscular disease classification and diagnosis

项目摘要 需要改进神经肌肉疾病的床旁诊断和评估方法。 一种用于此目的的技术是电阻抗肌描记术（EIM）。在EIM中， 一个非常微弱的高频电流通过感兴趣的肌肉和所产生的表面 测量电压。疾病相关的改变，在组成和微观结构特征的， 肌肉产生特征性变化，可用于帮助分类特定条件和分级疾病 严重性。迄今为止，大多数使用EIM分析的研究都使用了相当有限的数据集进行疾病评估。 虽然有效，但这种方法忽略了锁定在阻抗数据中的大量信息，包括 这些值可以帮助预测特定的肌肉特征（如肌纤维直径）和存在 病理变化（例如，脂肪或结缔组织沉积）。此外，目前的数据集是 临床医生在没有阻抗理论的详细知识的情况下难以理解。Myolex，Inc是一家 位于马萨诸塞州波士顿的一家小型企业将EIM技术的开发作为其主要重点， 临床应用。Myolex最近完成了第1阶段SBIR，展示了机器的潜在能力 基于学习的分类算法，以有效区分健康肌肉与患病肌肉， 区分一种疾病和另一种疾病。在这项拟议的工作中，我们将大大推进这一概念， 将分类算法融入到Myolex当前EIM系统的强大新软件套件中， mView的。我们的基本假设是，EIM数据分析可以自动化到分类的程度 系统可以提供关于疾病诊断以及疾病严重程度的数据，以提高易用性。我们 我建议通过两个具体目标来研究这个假设。在具体目标1中，我们将设计一个软件套件， 协助无伪影数据收集，并将其纳入我们当前的EIM系统mViewTM。然后 使用基于大量先前收集的数据的分类范例，我们将开发一个 自动化数据分析工具，帮助提供疾病类别以及微观特征、肌肉 基于阻抗数据单独使用微软的Azure云平台。在具体目标2中，我们将测试这一点 开发的软件套件在总共180名成人和儿童神经肌肉疾病患者和健康 参与者在俄亥俄州州立大学Wexner医学中心（成人）和波士顿儿童医院接受评估 （儿童）。在此数据收集期间，俄亥俄州和波士顿儿童的研究人员将有真实的- 与Myolex员工沟通，提供反馈，并回答和解决问题。的 将继续改进用户界面和分类算法。在这项工作结束时， 将开发一种新的诊断工具，以获得潜在的510（k）FDA批准。它将作为基础， 不断自我完善的系统作为额外的数据集收集的最终用户使用他们定期 临床应用。

Project Summary Improved methods for the bedside diagnosis and evaluation of neuromuscular disorders are needed. One technology that is finding increasing use for this purpose is electrical impedance myography (EIM). In EIM, a very weak, high frequency electrical current is passed through a muscle of interest and the resulting surface voltages are measured. Disease associated alterations in the composition and microstructural features of the muscle produce characteristic changes that can be used to help classify specific conditions and grade disease severity. To date, most studies using EIM analysis have utilized a fairly limited data set for disease assessment. While effective, this approach ignores a great deal of information locked within the impedance data, including those values that can assist in predicting specific muscle features (such as myofiber diameter) and the presence of pathological change (e.g., fat or connective tissue deposition). In addition, as it stands, the data set is challenging for the clinician to understand without a detailed knowledge of impedance theory. Myolex, Inc is a small business concern located in Boston, MA has as its main focus the development of EIM technologies for clinical use. Myolex recently completed a Phase 1 SBIR that demonstrated the potential capability of machine learning based classification algorithms to effectively discriminate healthy muscle from diseased and to discriminate one disease from another. In this proposed work, we will greatly advance this concept by embodying classification algorithms into a powerful new software suite for Myolex’s current EIM system, the mView. Our underlying hypothesis is that EIM data analysis can be automated to the point that classification systems can provide data on disease diagnosis as well as disease severity for improved ease-of-use. We propose to study this hypothesis via 2 specific aims. In Specific Aim 1, we will design a software suite capable of assisting with artifact-free data collection to be incorporated into our current EIM system, the mViewTM. Then using classification paradigms based on a prodigious amount of previous collected data, we will develop an automated data analysis tool to help provide data on disease category as well as microscopic features, muscle based on the impedance data alone using Microsoft’s Azure Cloud platform. In Specific Aim 2, we will test this developed software suite in a total of180 adult and pediatric neuromuscular disease patients and healthy participants evaluated at Ohio State University Wexner Medical Center (adults) and Boston Children’s Hospital (children). During this data collection period, the Ohio State and Boston Children’s researchers will have real- time access to Myolex staff to provide feedback and have questions/problems answered and addressed. The user interface will continue to be refined and classification algorithms improved. At the conclusion of this work, a new diagnostic tool will be developed for potential 510(k) FDA approval. It will serve as the basis for a continuously self-refining system as additional data sets are collected by end-users employing them in regular clinical use.