Abstract：
Currently, the use of autografts is the gold standard for the replacement of many damaged biological tissues. However, this practice presents disadvantages that can be mitigated through tissue-engineered implants. The aim of this study is to explore how machine learning can mechanically evaluate 2D and 3D polyvinyl alcohol (PVA) electrospun scaffolds (one twisted filament, 3 twisted filament and 3 twisted/braided filament scaffolds) for their use in different tissue engineering applications. Crosslinked and non-crosslinked scaffolds were fabricated and mechanically characterised, in dry/wet conditions and under longitudinal/transverse loading, using tensile testing. 28 machine learning models (ML) were used to predict the mechanical properties of the scaffolds. 4 exogenous variables (structure, environmental condition, crosslinking and direction of the load) were used to predict 2 endogenous variables (Young\'s modulus and ultimate tensile strength). ML models were able to identify 6 structures and testing conditions with comparable Young\'s modulus and ultimate tensile strength to ligamentous tissue, skin tissue, oral and nasal tissue, and renal tissue. This novel study proved that Classification and Regression Trees (CART) models were an innovative and easy to interpret tool to identify biomimetic electrospun structures; however, Cubist and Support Vector Machine (SVM) models were the most accurate, with R2 of 0.93 and 0.8, to predict the ultimate tensile strength and Young\'s modulus, respectively. This approach can be implemented to optimise the manufacturing process in different applications.
摘要：
目前,使用自体移植物是替换许多受损生物组织的黄金标准。然而,这种做法的缺点，可以减轻通过组织工程植入物。本研究的目的是探索机器学习如何机械评估2D和3D聚乙烯醇（PVA）静电纺支架（一根扭曲的长丝，3种扭曲的细丝和3种扭曲/编织的细丝支架）用于不同的组织工程应用。制造交联和非交联支架并进行机械表征，在干/湿条件下和在纵向/横向载荷下，使用拉伸测试。使用28个机器学习模型（ML）来预测支架的机械性能。4个外生变量(结构，环境条件,交联和载荷方向）用于预测2个内生变量（杨氏模量和极限拉伸强度）。ML模型能够识别具有与韧带组织相当的杨氏模量和极限拉伸强度的6种结构和测试条件，皮肤组织,口腔和鼻腔组织，和肾组织。这项新颖的研究证明，分类和回归树（CART）模型是一种创新且易于解释的工具，可以识别仿生电纺结构；但是，立体派和支持向量机(SVM)模型是最准确的，R2为0.93和0.8，以预测极限抗拉强度和杨氏模量，分别。可以实施该方法以优化不同应用中的制造过程。
