An open-source neurodynamic model of the lower urinary tract

Area of Science:

• Computational biology
• Biomedical engineering
• Urology

Background:

• Lower urinary tract symptoms (LUTS) are prevalent, necessitating advanced computational models for understanding and treatment.
• Existing computational models for LUTS are often closed-source, deterministic, and lack capabilities for neural intervention modeling.

Purpose of the Study:

• To develop an open-source, Python-based computational model simulating bladder, sphincter, and kidney dynamics.
• To enable prediction of pressure and volume using normalized neural signals.
• To provide a flexible and accessible tool for researchers and developers in silico medicine.

Main Methods:

• Developed an open-source Python model for bladder, sphincter, and kidney dynamics.
• Utilized normalized neural signals to predict pressure and volume.
• Verified the model against adult male Wistar rat bladder data and assessed noise sensitivity.

Main Results:

• The model demonstrated a significantly more similar pattern to animal data than existing models (r = 0.93, p < 0.001).
• All simulated physiological factors remained within physiological bounds.
• The model exhibited stability under noise conditions within described boundaries.

Conclusions:

• The developed open-source model advances computational medicine for LUTS research.
• It offers improved accessibility, a built-in neural model for bladder filling, and novel circadian-based kidney function.
• Future applications include personalized medicine and adaptive neural interventions for LUTS.