Optimizing electrical impedance myography outcomes through data mining

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R43AR073114-01
Agency Tracking Number: R43AR073114
Amount: $149,998.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: NIAMS
Solicitation Number: PA16-302
Solicitation Year: 2016
Award Year: 2017
Award Start Date (Proposal Award Date): 2017-09-20
Award End Date (Contract End Date): 2019-02-28
Small Business Information
5220 S UNIVERSITY DR UNIT 204C, Davie, FL, 33328-5308
DUNS: 831290577
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 (617) 242-0050
Business Contact
Phone: (617) 388-5550
Email: neil@skulpt.me
Research Institution
Project summary Electrical impedance myography EIM is a non invasive technology for the assessment of muscle that is based on the application of a weak high frequency electrical current to a muscle and the measurement of the resulting surface voltages The further development and application of EIM remains the main business focus of Skulpt Inc a small business concern based in Boston and San Francisco Specific Aims just say San Francisco Alterations to the condition of the muscle including myocyte atrophy fat and connective tissue deposition and inflammation all alter the EIM data in predictable and consistent ways To date through Skulpt EIM has been applied as a potential biomarker for assessing disease progression and response to therapy in a wide variety of neuromuscular disorders including amyotrophic lateral sclerosis Duchenne muscular dystrophy and spinal muscular atrophy as well as other disorders that impact muscle condition such as disuse atrophy and sarcopenia age related muscle loss over people have been studied with Skulpt s EIM technology Whereas the results of these applications are promising the analytic approaches taken to the data sets have been fairly basic utilizing only simple single frequency or simplistic multifrequency values However with every single muscle measurement over individual data points are acquired at different frequencies different depths of muscle penetration and at different angles to the major muscle fiber direction Moreover each of the above studies has been done in isolation and thus how results differ between diseases is unknown Given the plethora of data applying more sophisticated analytic approaches has the potential of yielding improved EIM measures Moreover collaborators have already collected an associated wealth of animal EIM data that will help further inform this analysis Thus in this proposed Phase SBIR we plan to apply a variety of data mining techniques to the vast set of data already accumulated at Skulpt Inc such that improved EIM outcomes can be developed and implemented In Specific Aim we will study human data across all disease types evaluated to determine which data sets are most effective at discriminating diseased from healthy muscle as well as distinguishing between diseases In Specific Aim we will focus on finding the metrics that are most sensitive to the degree of muscle pathology in a specific disease In both of these aims we will evaluate how these new metrics are mirrored in already obtained animal data In Specific Aim we will study these metrics in a new set of data a test set that was not used to develop the analytical paradigms so as to ensure their robustness With the conclusion of this work we will plan to pursue a Phase SBIR that will focus on the development of a software suite to assist in EIM data interpretation based upon these results followed by a prospective observational clinical study to evaluate the efficacy of these newly developed metrics for disease diagnosis and tracking of progression response to therapy Project Narrative Electrical impedance myography EIM is a non invasive technology for the assessment of muscle that remains the main focus of Skulpt Inc Considerable EIM data has already been collected in a variety of neuromuscular diseases In this study the investigators plan to perform a more detailed analysis of all data collected to date so called data mining such that improved EIM outcomes can be developed that will be applied to future studies

* Information listed above is at the time of submission. *

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