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Urination, or micturition involves the coordination of the bladder's detrusor muscle and two sphincters to ensure controlled bladder emptying.
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Related Experiment Video

Updated: May 10, 2025

Detrusor Underactivity Model in Rats by Conus Medullaris Transection
03:26

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Machine learning modeling and multi objective optimization of artificial detrusor.

Yin Mao1, Li Xiao2

  • 1School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, 510006, China.

Scientific Reports
|April 22, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel machine learning approach for optimizing artificial detrusors, enhancing urinary efficiency by 20% and reducing thermal risks by 62%. The method uses advanced algorithms for accurate modeling and multi-objective optimization, improving upon existing designs.

Keywords:
Artificial detrusorCrayfish optimization algorithmExtreme learning machineMulti-objective grey wolf optimization algorithmMulti-objective optimization design

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Single-objective optimization methods struggle to find optimal design parameters for artificial detrusors.
  • There is a need for improved modeling and optimization techniques to enhance artificial detrusor performance and safety.

Purpose of the Study:

  • To develop and validate a machine learning-based approach for artificial detrusor modeling and multi-objective optimization.
  • To improve the design parameters of artificial detrusors for enhanced urinary efficiency and reduced thermal tissue injury risks.

Main Methods:

  • Utilized Extreme Learning Machine (ELM) for artificial detrusor modeling.
  • Developed a multi-strategy modified crayfish optimization algorithm to tune ELM parameters for increased accuracy.
  • Employed a multi-objective grey wolf optimization algorithm for optimizing the artificial detrusor based on the ELM model.
  • Constructed an experimental platform with a shape memory spring-driven artificial detrusor for validation.

Main Results:

  • The modified crayfish optimization algorithm demonstrated superior performance and convergence compared to existing algorithms.
  • The artificial detrusor model accurately predicted emptying rate (RMSE: 1.51E-02) and temperature increment (RMSE: 8.47E-01), outperforming comparison models.
  • Optimized artificial detrusor showed a ~20% increase in emptying rate and a ~62% reduction in temperature increment, meeting engineering specifications with low experimental errors (7.8% and 11.8%).

Conclusions:

  • The proposed machine learning-based multi-objective optimization approach effectively models and optimizes artificial detrusors.
  • The optimized artificial detrusor design significantly enhances performance while mitigating risks of thermal tissue injury.
  • This research provides a robust framework for developing advanced artificial detrusor systems with improved functionality and safety.