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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Application of Machine Learning Algorithms to the Discretization Problem in Wearable Electrical Tomography Imaging

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Summary
This summary is machine-generated.

Artificial intelligence algorithms were adapted for Electrical Impedance Tomography (EIT) to discretize urinary tract data. This enables accurate bladder tracking and shape change monitoring for rehabilitation solutions.

Keywords:
decision treesdiscriminant analysiselastic netelectrical tomographyimage reconstructionlogistic regressionmachine learningnumerical calculationsensors

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Urinary tract monitoring requires precise discretization for accurate imaging.
  • Electrical Impedance Tomography (EIT) offers a non-invasive method for physiological monitoring.
  • Existing EIT methods need robust discretization for effective bladder tracking.

Purpose of the Study:

  • To implement and compare artificial intelligence (AI) algorithms for discretization in EIT for urinary tract monitoring.
  • To develop a finite element mesh (FEM) classifier for separating bladder (inclusion) from background.
  • To adapt supervised learning methods for tracking urinary bladder shape and position changes.

Main Methods:

  • Adaptation of supervised learning algorithms: logistic regression, decision trees, linear and quadratic discriminant analysis.
  • Development of a finite element mesh (FEM) classifier for image discretization.
  • Implementation of AI algorithms to supplement inverse problem solutions in EIT.
  • Design and demonstration of a novel EIT device for urinary tract monitoring.

Main Results:

  • Demonstrated successful adaptation of AI algorithms for EIT-based bladder discretization.
  • Showcased the capability of developed algorithms to track bladder placement and shape changes.
  • Validated the effectiveness of the developed EIT device and accompanying IT solutions.
  • Presented a robust measurement solution using sensors and statistical methods for bladder monitoring.

Conclusions:

  • AI-driven discretization significantly enhances EIT capabilities for urinary bladder tracking.
  • The developed EIT system provides effective information on bladder dynamics for rehabilitation.
  • This approach addresses a strong market demand for advanced urinary tract monitoring solutions.