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Proof-of-Concept of Microwave-Based Bladder State Detection Using Realistic Pelvic Models.
Farshkaran, Ali; Fry, Andrew; Raterink, Alex; Santorelli, Adam; Porter, Emily.
Afiliação
  • Farshkaran A; Department of Electrical and Computer EngineeringThe University of Texas at Austin Austin TX 78712 USA.
  • Fry A; Department of Electrical and Computer EngineeringThe University of Texas at Austin Austin TX 78712 USA.
  • Raterink A; Department of Electrical and Computer EngineeringThe University of Texas at Austin Austin TX 78712 USA.
  • Santorelli A; Rice University Houston TX 77005 USA.
  • Porter E; Department of Biomedical EngineeringThe University of Texas at Austin Austin TX 78712 USA.
IEEE Open J Eng Med Biol ; 5: 140-147, 2024.
Article em En | MEDLINE | ID: mdl-38445237
ABSTRACT
Goal Urinary incontinence (UI) affects a significant proportion of the population and is associated with negative physical and psychological side-effects. Microwave-based technologies may have the potential to monitor bladder volume, providing a proactive, low-cost and non-invasive tool to support individuals with UI.

Methods:

Studies to date on microwave bladder monitoring have been limited to highly simplified computational and experimental scenarios. In this work, we study the most realistic models to date (both male and female), which incorporate dielectrically and anatomically representative tissues of the pelvic region.

Results:

We examine the ability of detecting bladder fullness through both reflection and transmission-based parameters and, for the first time, study the effect of urine permittivity. As a proof-of-concept of bladder state detection, we further investigate reconstructing differential radar images of the bladder with two different volumes of urine.

Conclusions:

The results indicate that there is strong potential for monitoring and detecting the bladder state using microwave measurements.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article