Discrete element method-based simulation system for predicting natural stone evacuation pathways in patients with...

Area of Science:

• Biomedical Engineering
• Computational Modeling
• Urology

Background:

• Urolithiasis (kidney stones) is a common condition with high recurrence.
• Spontaneous stone passage is the preferred treatment, but mechanisms are poorly understood.
• Predicting stone passage is crucial for non-invasive management.

Purpose of the Study:

• To develop and validate a simulation system for predicting kidney stone trajectories.
• To elucidate the mechanisms of spontaneous stone passage using computational modeling.
• To enhance the accuracy of stone trajectory predictions for improved urolithiasis treatment.

Main Methods:

• Development of a kidney stone trajectory prediction system using the discrete element method (DEM).
• Comparison and optimization of DEM parameters (collision margin, friction, restitution coefficients) against 3D kidney replica experiments.
• Validation of the simulation system using diverse kidney shapes, stone positions, and sizes.

Main Results:

• The DEM simulation system accurately predicted stone trajectories within kidney replicas.
• Optimized DEM parameters demonstrated high fidelity across various kidney anatomies and stone characteristics.
• The simulation showed consistent reliability, irrespective of initial stone conditions or kidney shape.

Conclusions:

• The developed kidney stone simulation system is a reliable tool for predicting stone passage.
• This validated system offers potential for advancing non-invasive urolithiasis treatments.
• Improved trajectory prediction accuracy can guide clinical decisions and therapeutic strategies.