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

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要需要改进的床边诊断方法和神经肌肉疾病的评估。一项发现为此目的使用越来越多的技术的技术是电阻止myagraphy（EIM）。在EIM中，非常弱的高频电流通过感兴趣的肌肉和结果表面测量电压。疾病相关的变化在组成和微观结构特征的变化肌肉产生的特征变化，可用于帮助分类特定疾病和等级疾病严重程度。迄今为止，大多数使用EIM分析的研究都利用了相当有限的数据集进行疾病评估。尽管有效，但这种方法忽略了锁定在阻抗数据中的大量信息，包括那些可以帮助预测特定肌肉特征（例如肌纤维直径）和存在的值病理变化（例如脂肪或连接的组织沉积）。另外，按照目前的影响，数据集为在没有详细的阻抗理论知识的情况下，要挑战临床理解。 Myolex，Inc是位于马萨诸塞州波士顿的小型企业关注是其主要重点是EIM技术的开发临床用途。 Myolex最近完成了一个1期SBIR，该SBIR证明了机器的潜在能力基于学习的基于学习的分类算法，可有效区分健康的肌肉与失明和区分一种疾病。在这项拟议的工作中，我们将通过将分类算法体现为迈奥尔克斯当前的EIM系统的功能强大的新软件套件， mview。我们的基本假设是可以将EIM数据分析自动化至分类系统可以提供有关疾病诊断以及疾病严重程度的数据，以提高易用性。我们通过2个特定目的研究这一假设的建议。在特定目标1中，我们将设计一个能够软件套件协助将无伪影数据收集的内容纳入我们当前的EIM系统MviewTM。然后使用基于大量以前收集的数据的分类范例，我们将开发一个自动数据分析工具，以帮助提供有关疾病类别以及微观特征的数据，肌肉仅使用Microsoft的Azure Cloud平台，仅基于阻抗数据。在特定目标2中，我们将测试在总共有180名成人和小儿神经肌肉疾病患者和健康的软件套件中开发了软件套件俄亥俄州立大学韦克斯纳医学中心（成人）和波士顿儿童医院评估的参与者（孩子们）。在此数据收集期间，俄亥俄州和波士顿儿童研究人员将拥有现实时间访问Myolex员工以提供反馈，并有回答和解决的问题/问题。这用户界面将继续进行完善，并改善了分类算法。在这项工作结束时，将开发一种新的诊断工具，用于潜在的510（k）FDA批准。它将作为随着最终用户的常规使用者收集了其他数据集，该系统会连续自我缩放系统临床用途。