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

用于神经肌肉疾病分类和诊断的集成电阻抗肌电图平台

• 批准号: 10002324
• 负责人: Elmer C Lupton
• 金额: $ 86.97万
• 依托单位: MYOLEX, INC.
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-20 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10002324
• 关键词: Address; Adult; Affect; Algorithmic Software; Amyotrophic Lateral Sclerosis; Area; Back Pain; Boston; Businesses; Caliber; Categories; Characteristics; Child; Childhood; Classification; Clinical; Complex; Computer software; Connective Tissue; Data; Data Analyses; Data Analytics; Data Collection; Data Set; Deposition; Development; Diagnosis; Disease; Duchenne muscular dystrophy; Effectiveness; Electrodes; Ensure; Evaluation; Fatty acid glycerol esters; Feedback; Fiber; Frequencies; Functional disorder; Health; Inclusion Body Myositis; Individual; Knowledge; Machine Learning; Measurement; Measures; Medical; Medical Records; Medical Technology; Medical center; Methods; Microscopic; Morphologic artifacts; Muscle; Muscular Dystrophies; Myography; Myopathy; Myositis; Nerve; Neuromuscular Diseases; Neuromuscular conditions; Ohio; Outcome; Participant; Pathologic; Patients; Pediatric Hospitals; Performance; Phase; Physicians; Play; Positioning Attribute; Provider; Radiculopathy; Research Personnel; Role; Severities; Severity of illness; Small Business Innovation Research Grant; Specific qualifier value; Spinal Muscular Atrophy; Surface; System; Techniques; Technology; Testing; Time; Universities; Work; advanced analytics; base; classification algorithm; cloud based; cloud platform; commercialization; complex data; data acquisition; design; diagnosis evaluation; disease classification; disease diagnosis; electric impedance; feature extraction; improved; indexing; interest; machine learning algorithm; method development; nerve injury; neuromuscular; novel diagnostics; pediatric patients; physical therapist; prototype; sarcopenia; software development; success; theories; tool; usability; user friendly software; user-friendly; voltage

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.

项目摘要 需要改进神经肌肉疾病的床边诊断和评估方法。 有一种技术正越来越多地用于这一目的，那就是电阻抗肌图(EIM)。在EIM中， 一种非常微弱的高频电流通过感兴趣的肌肉和由此产生的表面 测量电压。与疾病相关的成分和显微结构特征的变化 肌肉产生的特征变化可以用来帮助对特定情况进行分类和对疾病分级 严肃性。到目前为止，大多数使用EIM分析的研究都利用了相当有限的数据集来进行疾病评估。 虽然这种方法很有效，但它忽略了阻抗数据中锁定的大量信息，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 病理改变(如脂肪或结缔组织沉积)。此外，目前的数据集是 对于临床医生来说，在没有详细的阻抗理论知识的情况下理解是具有挑战性的。Myolex，Inc.是 位于马萨诸塞州波士顿的小型企业公司将EIM技术的开发作为主要重点 临床应用。Myolex最近完成了第一阶段SBIR，展示了机器的潜在能力 基于学习的分类算法有效区分健康肌肉和疾病肌肉 区分一种疾病和另一种疾病。在这项拟议的工作中，我们将通过以下方式大大推进这一概念 将分类算法集成到Myolex当前EIM系统的强大新软件套件中， MView。我们的基本假设是，EIM数据分析可以自动化到分类的程度 系统可以提供有关疾病诊断和疾病严重程度的数据，以提高易用性。我们 建议通过两个具体目标来研究这一假说。在具体目标1中，我们将设计一个能够 帮助将无伪像数据收集整合到我们当前的EIM系统mViewTM中。然后 使用基于之前收集的大量数据的分类范例，我们将开发一种 自动数据分析工具，帮助提供有关疾病类别以及显微特征、肌肉 仅基于使用微软的Azure云平台的阻抗数据。在特定的目标2中，我们将测试这一点 开发的软件套件总共适用于180名成人和儿童神经肌肉疾病患者和健康 参与者在俄亥俄州立大学韦克斯纳医学中心(成人)和波士顿儿童医院接受评估 (儿童)。在此数据收集期间，俄亥俄州立大学和波士顿儿童研究人员将有真正的- 有时间与Myolex员工联系，提供反馈，并解答和解决问题。这个 用户界面将继续改进，分类算法将继续改进。在这项工作结束时， 将开发一种新的诊断工具，可能获得FDA的510(K)批准。它将作为一个 随着定期使用其他数据集的最终用户收集额外的数据集，系统不断自我完善 临床应用。