Machine learning applications in scaffold based bioprinting

Abstract

Bioprinting has tremendous potential to fabricate biomimetic architectures or living tissue constructs for applications in cell biology, tissue engineering and drug screening. Bioprinted constructs, called scaffolds have been extensively used in 3D cell culture systems to establish in vitro models. Diverse materials and printing mechanisms are involved in bioprinting, especially extrusion-based printing and electrohydrodynamic printing. With traditional theoretical and analytical models, it is difficult to build a scaffold biological/mechanical performance model using process parameters, fabrication structures and material properties. To link process-material-performance in a specific bioprinting process, various machine learning (ML) algorithms have been introduced because of their strong computational capability in pattern recognition and regression analysis. This paper overviews the status and progress of ML applications from several aspects: parameter optimization in the fabrication model, in situ monitoring and control, scaffold performance evaluation, and material design. Current challenges and potential solutions are also outlined, which may possibly pave the way to create a general framework that digitally integrates scaffold design, bioprinting, and performance evaluation.