Machine learning and 3D bioprinting

Abstract

With the growing number of biomaterials and printing technologies, bioprinting has brought about tremendous potential to fabricate biomimetic architectures or living tissue constructs. To make bioprinting and bioprinted constructs more powerful, machine learning (ML) is introduced to optimize the relevant processes, applied materials, and mechanical/biological performances. The objectives of this work were to collate, analyze, categorize, and summarize published articles and papers pertaining to ML applications in bioprinting and their impact on bioprinted constructs, as well as the directions of potential development. From the available references, both traditional ML and deep learning (DL) have been applied to optimize the printing process, structural parameters, material properties, and biological/ mechanical performance of bioprinted constructs. The former uses features extracted from image or numerical data as inputs in prediction model building, and the latter uses the image directly for segmentation or classification model building. All of these studies present advanced bioprinting with a stable and reliable printing process, desirable fiber/droplet diameter, and precise layer stacking, and also enhance the bioprinted constructs with better design and cell performance. The current challenges and outlooks in developing process–material–performance models are highlighted, which may pave the way for revolutionizing bioprinting technologies and bioprinted construct design.

Applications, advancements, and challenges of 3D bioprinting in organ transplantation

“…(b) Predictive modeling: machine learning models can be trained to predict the behavior and properties of bioinks based on their composition, facilitating the design of bioinks tailored for specific printing applications. 134 4.3.2 Printing optimization. (a) Print path planning: deep learning algorithms can optimize the printing process by learning from large datasets of printing parameters and outcomes to generate efficient print paths.…”

“…For instance, during the process of bioprinting, the materials or bioinks should be calculated according to the printing purpose and the whole process should be supervised about the qualification and so on. 134 Accordingly, as mentioned before, the mathematical models or scientific research equations are inefficient. In order to work out this insufficiency, deep learning (DL)/machine learning (ML) methods provide a promising solution.…”

“…(b) Predictive modeling: machine learning models can be trained to predict the behavior and properties of bioinks based on their composition, facilitating the design of bioinks tailored for specific printing applications. 134…”

In Situ Bioprinting: Process, Bioinks, and Applications

“…39,139,140 Machine learning (ML) has also been applied to optimize the printing process, structural parameters, material properties, and biological/mechanical performance of bioprinted constructs, leading to advanced bioprinting with stable and reliable printing processes and enhanced cell performance. 141 4.2. Bone.…”

“…Portable hand-held bioprinters and robotic arms have been developed for wound healing. It offers instant in situ gelation and enhanced bioadhesive performance. ,, Machine learning (ML) has also been applied to optimize the printing process, structural parameters, material properties, and biological/mechanical performance of bioprinted constructs, leading to advanced bioprinting with stable and reliable printing processes and enhanced cell performance …”