# A new hybrid modeling framework combining biophysics and deep learning to predict and optimize peripheral neuromodulation outcomes in lower urinary tract disease

> **NIH NIH R01** · FLORIDA INTERNATIONAL UNIVERSITY · 2023 · $602,938

## Abstract

Project Summary
 There is huge potential benefit for peripheral neuromodulation to treat lower urinary tract (LUT) dysfunction
through highly targeted interventions. But development and optimization of therapies have been slow, we
believe, because we lack the ability to predict the system level, functional response of the LUT to different types
and parameterizations of nerve stimulation. Without such an ability, the only recourse is to explore the vast space
of possible neuromodulation therapies in animal models, which is slow and expensive. The goal of this project
is to invent a predictive model that can assess orders of magnitude more parameterizations through computer
simulation, so we can then focus costly experimental efforts on the most promising computationally identified
candidates.
 To achieve this, we will create a framework that unites two powerful modeling approaches: first-principal
biophysics models and data-driven deep learning. The biophysics models let us precisely and powerfully
represent all the physiology that we understand quantitatively in a way that is both generalizable and
understandable. The problem with only using this approach, however, are the many parts of the LUT that we do
not understand with this level of confidence and detail. We will insert deep neural networks into the model
structure to statistically approximate the less well-understood LUT physiology. We will integrate both approaches
together in a single unified hybrid model, and train (tune parameter weights) the entire hybrid model at once with
data from cystometry experiments. In this way, we retain the power of biophysics-based models while
simultaneously reducing the size (and therefore data requirements) of the neural networks that need to be
trained. The neural networks will also be constrained by our LUT physiology knowledge, because they are linked
directly with biophysics-based models during simulation and training. We call the framework biomechanistic
learning augmentation of deep differential equation representations, or BLADDER.
 In this project we will first design and validate the BLADDER modeling framework using existing biophysics-
based models of LUT organs and training the neural network approximations on data from physiologically
nominal cystometry studies. We will then expand the hybrid model’s generalizability and robustness by
manipulating the biophysics-based models to allow us to train on data from a wide array of experimental contexts.
Finally, we will use the expanded-context model to make predictions about the contributing physiological factors
and optimal neuromodulation therapies for underactive bladder syndrome, a highly prevalent LUT dysfunction
without adequate treatment options. Our project goal is to develop and validate the BLADDER framework, then
use it to make clinically useful predictions for underactive bladder treatment. Our long term goals are to apply
the BLADDER approach to many LUT dysfunctions that could benefit...

## Key facts

- **NIH application ID:** 10705188
- **Project number:** 5R01DK133605-02
- **Recipient organization:** FLORIDA INTERNATIONAL UNIVERSITY
- **Principal Investigator:** Zachary C Danziger
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $602,938
- **Award type:** 5
- **Project period:** 2022-09-15 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10705188

## Citation

> US National Institutes of Health, RePORTER application 10705188, A new hybrid modeling framework combining biophysics and deep learning to predict and optimize peripheral neuromodulation outcomes in lower urinary tract disease (5R01DK133605-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10705188. Licensed CC0.

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