神经引导管道的智能设计:神经生长建模与优化的人工智能驱动流体结构相互作用研究

Faridoddin Hassani, Ali Golshani, Raman Mehrabi, Afshin Kouhkord, Mojtaba Guilani, Mahkame Sharbatdar
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摘要

神经引导导管(NGCs)已被证明能有效促进多种临床应用中的神经再生,包括创伤或手术导致的神经缺损。通过为神经生长提供有利环境,NGCs 可以帮助神经受损患者恢复功能。所面临的挑战包括修复长度有限、难以复制天然神经以及物质降解过快影响神经营养因子的输送。考虑到这些问题与传质和流体结构相互作用(FSI)的关系,提高神经再生效率的必要性就显得尤为重要。为了促进神经生长并输送适量的生长因子,这些导管的设计需要具备特定的拓扑、机械和生物特性。此外,还必须考虑功能性传质 FSI 设计。本文提出了一种基于评估优化和人工智能的智能 NGC 设计方法。研究发现,设计参数对优化的物理特性有重大影响,包括水压、孔隙率、扩散率、吸水性和最大应力。基于数据建模获得的数学代用模型被用于人工智能(AI)优化算法、微分进化算法(DE)和非支配排序遗传算法 II(NSGA-II)。结果表明,微分进化算法和非支配排序遗传算法 II(NSGA-II)产生的解决方案几乎完全相同,从而确保了 ML 优化的稳健性。结果表明,厚度为 750 μm 的 NGC 可使孔隙率增加 170% 以上。此外,在椭圆度不变的情况下,增加通道厚度可使最大应力增加 39.2% 以上。准确预测神经生长因子在 NGC 上的物理特性,为未来临床治疗神经损伤和先进的组织工程学带来了希望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent design of nerve guidance conduits: An artificial intelligence‐driven fluid structure interaction study on modelling and optimization of nerve growth
Nerve guidance conduits (NGCs) have been shown to be effective in promoting nerve regeneration in a variety of clinical applications, including nerve defects resulting from a trauma or surgery. By providing a conducive environment for nerve growth, NGCs can help to restore function in nerve‐damaged patients. Challenges include limited repair length, difficulty replicating natural nerve, and rapid substance degradation affecting neurotrophic factor delivery. Considering these issues with mass transfer and fluid structure interaction (FSI) emphasizes the need for enhancing nerve regeneration efficiency. To facilitate nerve growth and deliver appropriate amount of growth factors, these conduits need to be designed with specific topological, mechanical, and biological properties. Additionally, considerations must be given to functional mass transfer FSI design. An intelligent NGC design is proposed as an evaluation‐optimization and AI‐based method. It is found that design parameters significantly impact the physical properties being optimized, including hydraulic pressure, porosity, diffusivity, water absorption, and maximum stress. The mathematical surrogate model obtained from data‐based modelling is used for artificial intelligence (AI) optimization algorithms, differential evolution (DE), and non‐dominated sorting genetic algorithm II (NSGA‐II). It is revealed that both DE and NSGA algorithms generate nearly identical solutions, ensuring the robustness of ML optimization. Our results show that NGC with the thickness of 750 μm results in more than 170% augmentation of porosity. Moreover, at a constant ovality, increasing the channel thickness results in more than 39.2% augmentation of the maximum stress. The accurate forecasting of physical characteristics on NGC regarding nerve growth factors enables a hopeful outlook for the future clinical treatment of nerve injuries and advanced tissue engineering.
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