Literature DB >> 31358396

Assessment of sub-epidermal moisture by direct measurement of tissue biocapacitance.

Graham Ross1, Amit Gefen2.   

Abstract

The noninvasive SEM Scanner technology described herein assesses the fluid contents of human skin and subdermal tissues to a depth of several millimeters. The device makes a direct steady-state measurement of the capacitance of its sensor, which is affected by the equivalent dielectric constant of the material (i.e. the layered tissue structures) that is within the electric field between the sensor electrodes. Calculation of a "delta" value that compares measurements from several sites, some of which will be healthy tissue, compensates for systemic changes and provides a consistent measure of tissue health condition. We describe the hardware, software and rigorous laboratory testing and computational modeling of the principles of operation of the SEM Scanner, for the first time in the literature. These studies revealed a detection depth of approximately 4 mm for an electric potential of 0.3 V. The novel SEM Scanner provides the first useful technological means to assess the health status of tissues below the stratum corneum in patients who are at-risk for pressure injuries.
Copyright © 2019. Published by Elsevier Ltd.

Entities:  

Keywords:  Laboratory testing and modeling; Localized edema; Pressure injury; Pressure ulcer prevention; SEM scanner

Mesh:

Substances:

Year:  2019        PMID: 31358396     DOI: 10.1016/j.medengphy.2019.07.011

Source DB:  PubMed          Journal:  Med Eng Phys        ISSN: 1350-4533            Impact factor:   2.242


  4 in total

1.  Sensitivity and laboratory performances of a second-generation sub-epidermal moisture measurement device.

Authors:  Lea Peko; Amit Gefen
Journal:  Int Wound J       Date:  2020-03-11       Impact factor: 3.315

2.  A blinded clinical study using a subepidermal moisture biocapacitance measurement device for early detection of pressure injuries.

Authors:  Henry Okonkwo; Ruth Bryant; Jeanette Milne; Donna Molyneaux; Julie Sanders; Glen Cunningham; Sharon Brangman; William Eardley; Garrett K Chan; Barbara Mayer; Mary Waldo; Barbara Ju
Journal:  Wound Repair Regen       Date:  2020-01-21       Impact factor: 3.617

3.  A machine learning algorithm for early detection of heel deep tissue injuries based on a daily history of sub-epidermal moisture measurements.

Authors:  Maayan Lustig; Dafna Schwartz; Ruth Bryant; Amit Gefen
Journal:  Int Wound J       Date:  2022-01-12       Impact factor: 3.099

4.  Our contemporary understanding of the aetiology of pressure ulcers/pressure injuries.

Authors:  Amit Gefen; David M Brienza; Janet Cuddigan; Emily Haesler; Jan Kottner
Journal:  Int Wound J       Date:  2021-08-11       Impact factor: 3.315

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.