An open-source neurodynamic model of the lower urinary tract.

Lower urinary tract symptoms affect a significant proportion of the population. In silico medicine can help understand these conditions and develop treatments. However, many of the current lower urinary tract computational models are closed source, too deterministic and do not allow for simple use of modelling neural intervention. An open-source Python-based model was developed to simulate bladder, sphincter and kidney dynamics using normalized neural signals to predict pressure and volume. The model was verified against animal bladder data from adult male Wistar rats, assessed for noise sensitivity and evaluated against known physiological factors. The animal data comparison yielded a significantly more similar pattern than existing models, with a correlation coefficient of r = 0.93 (p < 0.001). All physiological factors were within bounds, and the model remained stable with noise under the described boundaries. The proposed model advances the field of computational medicine by providing an open-source model for researchers and developers. It improves upon existing models by being accessible, including a built-in neural model that better replicates smooth bladder filling results, and incorporating a novel kidney function that alters bladder function by time of day in line with circadian rhythm. Future applications include personalized medicine, treating lower urinary tract symptoms with in silico models and adaptive neural interventions.