目的:软生物组织的先进材料模型和材料表征在血管手术和经导管介入的术前计划中起着至关重要的作用。心脏瓣膜工程的最新进展,医疗设备和贴片设计建立在这些模型上。此外,了解天然和组织工程血管生物材料中的血管生长和重塑,以及在软组织上设计和测试药物,是预测再生医学的关键方面。数十年来,传统的非线性优化方法和有限元(FE)模拟一直是与软组织力学和拉伸测试相结合的生物材料表征工具。然而,通过非线性优化方法获得的结果只有在一定程度上是可靠的,由于数学上的限制,和有限元模拟可能需要大量的计算时间和资源,这对于特定于患者的模拟可能是不合理的。在很大程度上,近年来,机器学习(ML)技术在软组织力学领域的应用越来越突出,与传统方法相比,具有显著的优势。本文对用于估计软生物组织和生物材料的机械特性的新兴ML算法进行了深入的研究。这些算法用于分析关键属性,例如应力-应变曲线和压力-体积回路。审查的重点是在心血管工程中的应用,并讨论了每种方法的基本数学基础。
方法:审查工作采用了两种策略。首先,积极从事心血管软组织力学的主要研究小组的最新研究被汇编,我们的综述中包括了利用ML和深度学习(DL)技术的研究论文。第二种策略涉及跨主要数据库的标准关键字搜索。这种方法提供了11篇相关的ML文章,从包括ScienceDirect在内的知名来源中精心挑选,Springer,PubMed,谷歌学者。选择过程涉及使用特定的关键词,如“机器学习”或“深度学习”,以及“软生物组织”,“心血管”,\"患者特异性,“应变能”,“血管”或“生物材料”。最初,共选出25篇。然而,排除了这些文章中的14篇,因为它们与专注于专门用于软组织修复和再生的生物材料的标准不一致。因此,其余11篇文章根据使用的ML技术和使用的训练数据进行分类.
结果:用于评估软生物组织和生物材料的机械特性的ML技术大致分为两类:标准ML算法和基于物理学的ML算法。然后,标准ML模型根据其任务进行组织,分为回归和分类子类别。在这些类别中,研究采用了各种监督学习模型,包括支持向量机(SVM),袋装决策树(BDT),人工神经网络(ANN)或深度神经网络(DNN),和卷积神经网络(CNN)。此外,利用无监督学习方法,例如结合主成分分析(PCA)和/或低秩近似(LRA)的自动编码器,基于训练数据的特定特征。训练数据主要包括三种类型:实验机械数据,包括单轴或双轴应力-应变数据;通过非线性拟合和/或FE模拟生成的合成机械数据;以及诸如3D二次谐波生成(SHG)图像或计算机断层扫描(CT)图像的图像数据。物理信息ML模型的性能评估主要取决于确定系数R2。相比之下,利用各种度量和误差度量来评估标准ML模型的性能。此外,我们的综述包括对普遍的生物材料模型的广泛研究,这些生物材料模型可以作为物理信息ML模型的物理定律.
结论:ML模型提供了准确的,快,和可靠的方法来评估病变的软组织段的力学特性和选择最佳的生物材料的时间关键的软组织手术。在这篇综述中研究的各种机器学习模型中,物理信息神经网络模型表现出准确预测软生物组织的力学响应的能力,即使训练样本有限。这些模型实现高R2值范围从0.90到1.00。考虑到与获得大量用于实验目的的活组织样本相关的挑战,这一点尤其重要。这可能是耗时且不切实际的。此外,这篇评论不仅讨论了当前文献中确定的优势,而且还阐明了局限性,并提供了对未来观点的见解。
OBJECTIVE: Advanced material models and material characterization of soft biological tissues play an essential role in pre-surgical planning for vascular surgeries and transcatheter interventions. Recent advances in heart valve engineering, medical device and patch design are built upon these models. Furthermore, understanding vascular growth and remodeling in native and tissue-engineered vascular biomaterials, as well as designing and testing drugs on soft tissue, are crucial aspects of predictive regenerative medicine. Traditional nonlinear optimization methods and finite element (FE) simulations have served as biomaterial characterization tools combined with soft tissue mechanics and tensile testing for decades. However, results obtained through nonlinear optimization methods are reliable only to a certain extent due to mathematical limitations, and FE simulations may require substantial computing time and resources, which might not be justified for patient-specific simulations. To a significant extent, machine learning (ML) techniques have gained increasing prominence in the field of soft tissue mechanics in recent years, offering notable advantages over conventional methods. This review article presents an in-depth examination of emerging ML algorithms utilized for estimating the mechanical characteristics of soft biological tissues and biomaterials. These algorithms are employed to analyze crucial properties such as stress-strain curves and pressure-volume loops. The focus of the review is on applications in cardiovascular engineering, and the fundamental mathematical basis of each approach is also discussed.
METHODS: The review effort employed two strategies. First, the recent studies of major research groups actively engaged in cardiovascular soft tissue mechanics are compiled, and research papers utilizing ML and deep learning (DL) techniques were included in our review. The second strategy involved a standard keyword search across major databases. This approach provided 11 relevant ML articles, meticulously selected from reputable sources including ScienceDirect, Springer, PubMed, and Google Scholar. The selection process involved using specific keywords such as \"machine learning\" or \"deep learning\" in conjunction with \"soft biological tissues\", \"cardiovascular\", \"patient-specific,\" \"strain energy\", \"vascular\" or \"biomaterials\". Initially, a total of 25 articles were selected. However, 14 of these articles were excluded as they did not align with the criteria of focusing on biomaterials specifically employed for soft tissue repair and regeneration. As a result, the remaining 11 articles were categorized based on the ML techniques employed and the training data utilized.
RESULTS: ML techniques utilized for assessing the mechanical characteristics of soft biological tissues and biomaterials are broadly classified into two categories: standard ML algorithms and physics-informed ML algorithms. The standard ML models are then organized based on their tasks, being grouped into Regression and Classification subcategories. Within these categories, studies employ various supervised learning models, including support vector machines (SVMs), bagged decision trees (BDTs), artificial neural networks (ANNs) or deep neural networks (DNNs), and convolutional neural networks (CNNs). Additionally, the utilization of unsupervised learning approaches, such as autoencoders incorporating principal component analysis (PCA) and/or low-rank approximation (LRA), is based on the specific characteristics of the training data. The training data predominantly consists of three types: experimental mechanical data, including uniaxial or biaxial stress-strain data; synthetic mechanical data generated through non-linear fitting and/or FE simulations; and image data such as 3D second harmonic generation (SHG) images or computed tomography (CT) images. The evaluation of performance for physics-informed ML models primarily relies on the coefficient of determination R 2 . In contrast, various metrics and error measures are utilized to assess the performance of standard ML models. Furthermore, our review includes an extensive examination of prevalent biomaterial models that can serve as physical laws for physics-informed ML models.
CONCLUSIONS: ML models offer an accurate, fast, and reliable approach for evaluating the mechanical characteristics of diseased soft tissue segments and selecting optimal biomaterials for time-critical soft tissue surgeries. Among the various ML models examined in this review, physics-informed neural network models exhibit the capability to forecast the mechanical response of soft biological tissues accurately, even with limited training samples. These models achieve high R 2 values ranging from 0.90 to 1.00. This is particularly significant considering the challenges associated with obtaining a large number of living tissue samples for experimental purposes, which can be time-consuming and impractical. Additionally, the review not only discusses the advantages identified in the current literature but also sheds light on the limitations and offers insights into future perspectives.