Kim Joohyun, McKee Jane A, Fontenot Jake J, Jung Jangwook P
Center for Computation and Technology, Louisiana State University, Baton Rouge, LA, United States.
Department of Biological Engineering, Louisiana State University, Baton Rouge, LA, United States.
Front Bioeng Biotechnol. 2020 Jan 10;7:443. doi: 10.3389/fbioe.2019.00443. eCollection 2019.
Regenerating lost or damaged tissue is the primary goal of Tissue Engineering. 3D bioprinting technologies have been widely applied in many research areas of tissue regeneration and disease modeling with unprecedented spatial resolution and tissue-like complexity. However, the extraction of tissue architecture and the generation of high-resolution blueprints are challenging tasks for tissue regeneration. Traditionally, such spatial information is obtained from a collection of microscopic images and then combined together to visualize regions of interest. To fabricate such engineered tissues, rendered microscopic images are transformed to code to inform a 3D bioprinting process. If this process is augmented with data-driven approaches and streamlined with machine intelligence, identification of an optimal blueprint can become an achievable task for functional tissue regeneration. In this review, our perspective is guided by an emerging paradigm to generate a blueprint for regeneration with machine intelligence. First, we reviewed recent articles with respect to our perspective for machine intelligence-driven information retrieval and fabrication. After briefly introducing recent trends in information retrieval methods from publicly available data, our discussion is focused on recent works that use machine intelligence to discover tissue architectures from imaging and spectral data. Then, our focus is on utilizing optimization approaches to increase print fidelity and enhance biomimicry with machine learning (ML) strategies to acquire a blueprint ready for 3D bioprinting.
再生丢失或受损组织是组织工程的主要目标。3D生物打印技术以前所未有的空间分辨率和类组织复杂性，已广泛应用于组织再生和疾病建模的许多研究领域。然而，提取组织结构和生成高分辨率蓝图对于组织再生来说是具有挑战性的任务。传统上，此类空间信息是从一系列微观图像中获取，然后组合在一起以可视化感兴趣的区域。为了制造此类工程组织，渲染后的微观图像被转换为代码，以指导3D生物打印过程。如果这个过程通过数据驱动方法得到增强，并通过机器智能进行简化，那么识别最佳蓝图对于功能性组织再生可能会成为一项可实现的任务。在本综述中，我们的观点受一种新兴范式的指导，即利用机器智能生成再生蓝图。首先，我们按照机器智能驱动的信息检索和制造这一观点，对近期文章进行了综述。在简要介绍了从公开可用数据中进行信息检索方法的近期趋势后，我们的讨论集中在利用机器智能从成像和光谱数据中发现组织结构的近期工作上。然后，我们关注利用优化方法提高打印保真度，并通过机器学习（ML）策略增强仿生学，以获取可供3D生物打印的蓝图。
