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

一种新的混合建模框架，结合生物物理学和深度学习来预测和优化下尿路疾病的周围神经调节结果

• 批准号: 10502727
• 负责人: Zachary C Danziger
• 金额: $ 66.03万
• 依托单位: FLORIDA INTERNATIONAL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10502727
• 关键词: Animal Model; Animals; Artificial Intelligence; Behavior; Biological; Biophysics; Bladder; Clinical; Complex; Computer Models; Computer Simulation; Consumption; Data; Data Set; Development; Differential Equation; Disease; Ensure; Etiology; Functional disorder; Generations; Goals; Hybrids; Intervention; Intervention Studies; Knowledge; Lead; Learning; Link; Lower urinary tract; Measurement; Measures; Methods; Modeling; Nerve; Organ; Outcome; Output; Peripheral; Peripheral Nerves; Persons; Physiological; Physiology; Process; Rattus; Reflex action; Reflex control; Sensory; Series; Severities; Site; Source; Structure; Symptoms; Syndrome; System; Testing; Therapeutic; Time; Tissues; Training; Uncertainty; Update; Urethra; Urinary Tract Physiology; Urinary tract; Urologic Diseases; Validation; Weight; Work; age related; aged; artificial neural network; base; biophysical model; cohort; computer framework; cost; deep learning; deep neural network; design; effective therapy; experimental study; in vivo; insight; intervention effect; learning network; mathematical model; nerve transection; neural network; neuroregulation; novel; predictive modeling; relating to nervous system; response; side effect; simulation; standard care; theories; tool; treatment optimization

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.

项目摘要 周围神经调节治疗下尿路功能障碍具有巨大的潜在益处 通过有针对性的干预。但是，治疗方法的开发和优化一直很缓慢，我们 我相信，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 和神经刺激的参数化。如果没有这样的能力，唯一的办法就是探索浩瀚的太空 在动物模型中进行可能的神经调节疗法，这是缓慢和昂贵的。这个项目的目标 是发明一种预测模型，可以通过计算机评估数量级更多的参数， 模拟，因此我们可以将昂贵的实验工作集中在最有希望的计算识别上 候选人 为了实现这一点，我们将创建一个框架，该框架将两种强大的建模方法结合在一起： 生物物理学模型和数据驱动的深度学习。生物物理学模型让我们精确而有力地 以一种既可概括又可量化的方式代表我们所理解的所有生理学， 可以理解的然而，只使用这种方法的问题是，我们所做的LUT的许多部分 无法以这种程度的信心和细节来理解。我们将在模型中插入深度神经网络 结构，以统计近似不太好理解的LUT生理学。我们将整合这两种方法 一起形成一个统一的混合模型，并立即训练（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制药学的模型的力量， 同时减少了需要的神经网络的大小（因此也减少了数据需求）， 受到了培训神经网络也将受到我们的LUT生理学知识的限制，因为它们是相互联系的。 在模拟和训练期间直接与基于生物制药学的模型相结合。我们称之为生物力学框架 深度微分方程表示的学习增强，或膀胱。 在这个项目中，我们将首先设计和验证膀胱建模框架使用现有的生物物理学- 基于LUT器官的模型，并根据生理学数据训练神经网络近似， 标称膀胱测压研究。然后，我们将通过以下方式扩展混合模型的可推广性和鲁棒性： 操纵基于生物制药学的模型，使我们能够对来自各种实验环境的数据进行训练。 最后，我们将使用扩展上下文模型来预测生理因素 以及用于膀胱活动不足综合征的最佳神经调节疗法， 没有足够的治疗选择。我们的项目目标是开发和验证膀胱框架，然后 用它来为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 膀胱治疗许多LUT功能障碍，这些功能障碍也可以从神经调节治疗中受益， 与其他生理系统一样。