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

> **NIH NIH R44** · MYOLEX, INC. · 2020 · $869,698

## Abstract

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 develop...

## Key facts

- **NIH application ID:** 10002324
- **Project number:** 5R44NS113756-03
- **Recipient organization:** MYOLEX, INC.
- **Principal Investigator:** Elmer C Lupton
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $869,698
- **Award type:** 5
- **Project period:** 2017-09-20 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10002324

## Citation

> US National Institutes of Health, RePORTER application 10002324, An integrated electrical impedance myography platform for neuromuscular disease classification and diagnosis (5R44NS113756-03). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10002324. Licensed CC0.

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