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Related Concept Videos

Urinary Bladder01:23

Urinary Bladder

2.6K
The urinary bladder is a hollow, muscular sac that temporarily stores urine before it is expelled from the body. It can hold approximately 600 mL of urine prior to micturition. The bladder is retroperitoneal and located behind the pubic symphysis in the pelvic floor.
In males, the bladder is situated in front of the rectum, while in females, it is positioned anterior to the vagina and uterus. The bladder floor contains an inverted triangular area called the trigone, defined by the two ureteric...
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Related Experiment Video

Updated: Dec 13, 2025

A Decentralized Ex Vivo Murine Bladder Model with the Detrusor Muscle Removed for Direct Access to the Suburothelium during Bladder Filling
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A deep neural network for estimating the bladder boundary using electrical impedance tomography.

S K Konki1, A K Khambampati2, S K Sharma2

  • 1Center for Artificial Intelligence, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea.

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|July 30, 2020
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Summary

A deep neural network (DNN) accurately estimates urinary bladder size using electrical impedance tomography (EIT). This novel method improves upon traditional algorithms for better incontinence treatment.

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Machine Learning

Background:

  • Accurate bladder size estimation is crucial for treating urinary incontinence.
  • Electrical impedance tomography (EIT) is a non-invasive imaging technique for organ boundary estimation.
  • Traditional EIT inverse algorithms like mNR struggle with complex boundaries and initial guess dependency.

Purpose of the Study:

  • To introduce a deep neural network (DNN) for accurate urinary bladder boundary and size estimation using EIT.
  • To overcome the limitations of conventional EIT inverse solvers in complex shape approximation.

Main Methods:

  • A five-layer DNN was designed and trained using boundary voltage measurements as input and Fourier coefficients of the bladder boundary as output.
  • The DNN model was trained on data from 15 subjects with varying pelvic anatomy and bladder characteristics.
  • Performance was evaluated using numerical simulations, phantom experiments, and comparison with Radial Basis Function (RBF) and modified Newton-Raphson (mNR) methods.

Main Results:

  • The DNN demonstrated a low root mean square error in estimating boundary coefficients.
  • The DNN achieved superior bladder size estimation accuracy compared to mNR and RBF methods.
  • Numerical simulations and phantom experiments validated the DNN's performance.

Conclusions:

  • The developed DNN serves as an efficient EIT inverse solver for complex boundary estimation, specifically for the urinary bladder.
  • The DNN offers a simple, accurate, and fast alternative for estimating bladder size and shape.
  • This approach enhances clinical treatment for patients with urinary incontinence.