# Convenient quantification of myopathic change in muscle via electrical impedance myography

> **NIH NIH R44** · MYOLEX, INC. · 2024 · $1,006,120

## Abstract

Project Summary
 Magnetic resonance imaging (MRI) is considered an excellent technology for quantifying muscle
composition in a variety of disorders. MRI offers superb insights into muscle condition and pathology, including
muscle size, the amount of fat deposition, and the presence of edema or inflammation. It is now often used as
a tool in clinical trials to provide biomarkers for assessing disease progression and response to therapeutic
intervention disorders ranging from Duchenne muscular dystrophy to atrophy related to aging or injury.
However, MRI has major drawbacks as a muscle characterization tool, including inconvenience, high cost, the
requirement for patients to lie flat (a major challenge in patients with impaired respiratory function), the need
to standardize across systems, and the necessity for cumbersome image processing. A technology that could
offer much of what of MRI has to offer but with greater convenience, lower cost, and simplified analysis could
find wide application both for clinical trials but also ultimately for individual patient care. One technology that
could achieve this is electrical impedance myography (EIM). EIM has independently been shown to correlate
strongly with muscle pathology and to track disease progression and response to therapy. Myolex, Inc has
made the development and application of EIM its focus. In this direct-to-Phase 2 SBIR application, Myolex
proposes to establish EIM, via its new device, the mScan, as a valuable alternative to MRI for
assessment of muscle condition in primary myopathic disorders. We propose to achieve this by
performing EIM and MRI on patients with a variety of myopathies, including autoimmune and hereditary
conditions, and using machine learning to develop predictive algorithms relating EIM to MRI. Our underlying
hypothesis is that EIM data closely relates to muscle pathology as revealed by MRI and the simpler,
more convenient technology of EIM can be trained to provide MRI-like assessments of muscle
condition. In Specific Aim 1, in conjunction with physicians at Beth Israel Deaconess Medical Center,
Harvard Medical School, we will collect MRI and EIM data on a cohort of healthy subjects and patients with
primary myopathic conditions, including those with active muscle inflammation/ edema (secondary to myositis
and toxic myopathy) and more chronic conditions (including hereditary myopathies and muscular dystrophies)
of varying severity. Strength and functional data will also be collected. Using this data, in Specific Aim 2, we
will develop predictive algorithms, via the penalized regression technique of Lasso (least absolute shrinkage
and selection operator), leveraging EIM values to predict MRI findings. These findings will also be associated
with functional measures. Successful algorithms will be incorporated into a cloud-based diagnostic engine to
provide quantifiable data on muscle disease pathology. At the conclusion of this work, we will have developed
an accurate, qua...

## Key facts

- **NIH application ID:** 10921274
- **Project number:** 1R44AR083316-01A1
- **Recipient organization:** MYOLEX, INC.
- **Principal Investigator:** David P. Dickinson
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,006,120
- **Award type:** 1
- **Project period:** 2024-06-15 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10921274

## Citation

> US National Institutes of Health, RePORTER application 10921274, Convenient quantification of myopathic change in muscle via electrical impedance myography (1R44AR083316-01A1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10921274. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*