Bermejillo Barrera María Dolores, Franco-Martínez Francisco, Díaz Lantada Andrés
ETSI de Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense 30, 28040 Madrid, Spain.
Mechanical Engineering Department, ETSI Industriales, Universidad Politécnica de Madrid, Calle José Gutiérrez Abascal 2, 28006 Madrid, Spain.
Materials (Basel). 2021 Sep 14;14(18):5278. doi: 10.3390/ma14185278.
Design requirements for different mechanical metamaterials, porous constructions and lattice structures, employed as tissue engineering scaffolds, lead to multi-objective optimizations, due to the complex mechanical features of the biological tissues and structures they should mimic. In some cases, the use of conventional design and simulation methods for designing such tissue engineering scaffolds cannot be applied because of geometrical complexity, manufacturing defects or large aspect ratios leading to numerical mismatches. Artificial intelligence (AI) in general, and machine learning (ML) methods in particular, are already finding applications in tissue engineering and they can prove transformative resources for supporting designers in the field of regenerative medicine. In this study, the use of 3D convolutional neural networks (3D CNNs), trained using digital tomographies obtained from the CAD models, is validated as a powerful resource for predicting the mechanical properties of innovative scaffolds. The presented AI-aided or ML-aided design strategy is believed as an innovative approach in area of tissue engineering scaffolds, and of mechanical metamaterials in general. This strategy may lead to several applications beyond the tissue engineering field, as we analyze in the discussion and future proposals sections of the research study.
用作组织工程支架的不同机械超材料、多孔结构和晶格结构的设计要求，由于它们要模仿的生物组织和结构具有复杂的机械特性，因而导致了多目标优化。在某些情况下，由于几何复杂性、制造缺陷或导致数值不匹配的大长宽比，无法应用传统的设计和模拟方法来设计此类组织工程支架。一般而言的人工智能（AI），尤其是机器学习（ML）方法，已经在组织工程中得到应用，并且它们可以证明是支持再生医学领域设计师的变革性资源。在本研究中，使用从CAD模型获得的数字断层扫描进行训练的3D卷积神经网络（3D CNN），被验证为预测创新支架力学性能的强大资源。所提出的人工智能辅助或机器学习辅助设计策略被认为是组织工程支架领域以及一般机械超材料领域的一种创新方法。正如我们在研究的讨论和未来建议部分所分析的那样，这种策略可能会带来组织工程领域之外的多种应用。
