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The virtual physiological human gets nerves! How to account for the action of the nervous system in multiphysics simulations of human organs
Journal of The Royal Society Interface ( IF 3.9 ) Pub Date : 2021-04-14 , DOI: 10.1098/rsif.2020.1024
A Alexiadis 1 , M J H Simmons 1 , K Stamatopoulos 1, 2 , H K Batchelor 3 , I Moulitsas 4
Affiliation  

This article shows how to couple multiphysics and artificial neural networks to design computer models of human organs that autonomously adapt their behaviour to environmental stimuli. The model simulates motility in the intestine and adjusts its contraction patterns to the physical properties of the luminal content. Multiphysics reproduces the solid mechanics of the intestinal membrane and the fluid mechanics of the luminal content; the artificial neural network replicates the activity of the enteric nervous system. Previous studies recommended training the network with reinforcement learning. Here, we show that reinforcement learning alone is not enough; the input–output structure of the network should also mimic the basic circuit of the enteric nervous system. Simulations are validated against in vivo measurements of high-amplitude propagating contractions in the human intestine. When the network has the same input–output structure of the nervous system, the model performs well even when faced with conditions outside its training range. The model is trained to optimize transport, but it also keeps stress in the membrane low, which is exactly what occurs in the real intestine. Moreover, the model responds to atypical variations of its functioning with ‘symptoms’ that reflect those arising in diseases. If the healthy intestine model is made artificially ill by adding digital inflammation, motility patterns are disrupted in a way consistent with inflammatory pathologies such as inflammatory bowel disease.



中文翻译:

虚拟生理人会紧张!如何在人体器官的多物理场模拟中考虑神经系统的作用

本文介绍了如何将多物理场和人工神经网络结合起来,设计能够自动使其行为适应环境刺激的人体器官计算机模型。该模型模拟肠蠕动,并根据肠腔内容物的物理特性调整其收缩模式。多物理场再现了肠膜的固体力学和管腔内含物的流体力学。人工神经网络复制了肠神经系统的活动。先前的研究建议通过强化学习来训练网络。在这里,我们表明仅靠强化学习是不够的。网络的输入输出结构还应该模仿肠神经系统的基本电路。模拟已针对体内进行了验证在人体肠道中的高幅度传播收缩的测量。当网络具有与神经系统相同的输入输出结构时,即使面对训练范围之外的条件,该模型也能很好地运行。对模型进行了训练,以优化运输,但也可以使膜中的应力保持在较低水平,而这正是真实肠道中发生的情况。此外,该模型以“症状”来回应其功能的非典型变化,这些“症状”反映出疾病中产生的那些症状。如果通过添加数字炎症使健康的肠道模型人为患病,则会以与诸如炎症性肠病之类的炎症病理学一致的方式破坏运动模式。

更新日期:2021-04-14
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