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

项目摘要 周围神经调节治疗下尿路(LUT)功能障碍有巨大的潜在益处 通过高度针对性的干预。但治疗方法的开发和优化一直很缓慢，我们 相信，因为我们缺乏预测系统水平的能力，LUT对不同类型的功能反应 以及神经刺激的参数设置。如果没有这样的能力，唯一的办法就是探索广阔的太空 动物模型中可能的神经调节疗法，这是缓慢和昂贵的。这个项目的目标是 发明了一种预测模型，可以通过计算机对更多的参数进行数量级评估 模拟，这样我们就可以将昂贵的实验工作集中在最有希望通过计算识别的 候选人。 为了实现这一点，我们将创建一个框架，将两种强大的建模方法统一起来：第一主体 生物物理模型和数据驱动的深度学习。生物物理模型使我们能够准确而有力地 以一种既可概括又可量化的方式表示我们所理解的所有生理学 可以理解。然而，只使用这种方法的问题在于我们所做的LUT的许多部分 不能理解这种程度的自信和细节。我们将在模型中加入深度神经网络 结构来统计地近似了解较少的LUT生理学。我们将整合这两种方法 在单个统一的混合模型中一起使用，并一次性训练(调整参数权重)整个混合模型 来自膀胱测压实验的数据。通过这种方式，我们保留了基于生物物理学的模型的力量，同时 同时减少神经网络的大小(从而减少数据需求) 训练有素。神经网络也将受到我们LUT生理学知识的限制，因为它们是链接的 在模拟和训练期间直接使用基于生物物理学的模型。我们称这种框架为生物力学 学习加深的微分方程式表示，或膀胱。 在这个项目中，我们将首先使用现有的生物物理学来设计和验证膀胱建模框架- 基于LUT器官模型和基于生理学数据的神经网络逼近训练 名义上的膀胱测压研究。然后，我们将通过以下方式扩展混合模型的泛化能力和健壮性 操纵基于生物物理学的模型，使我们能够根据来自广泛实验环境的数据进行培训。 最后，我们将使用扩展上下文模型来预测起作用的生理因素 和最佳神经调节疗法治疗膀胱活动不足综合征，一种高度流行的LUT功能障碍 没有适当的治疗选择。我们的项目目标是开发和验证膀胱框架，然后 使用它来对膀胱活动不足的治疗做出临床上有用的预测。我们的长期目标是应用 膀胱途径治疗许多LUT功能障碍，也可以从神经调节治疗中受益 至于其他生理系统。

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 from neuromodulation treatments, as well as to other physiological systems.