Approximating scaffold printability utilizing computational methods

Abstract

Bioprinting facilitates the generation of complex, three-dimensional (3D), cell-based constructs for various applications. Although multiple bioprinting technologies have been developed, extrusion-based systems have become the dominant technology due to the diversity of materials (bioinks) that can be utilized, either individually or in combination. However, each bioink has unique material properties and extrusion characteristics that affect bioprinting utility, accuracy, and precision. Here, we have extended our previous work to achieve high precision (i.e., repeatability) and printability across samples by optimizing bioink-specific printing parameters. Specifically, we hypothesized that a Fuzzy Inference System (FIS) could be used as a computational method to address the imprecision in 3D bioprinting test data and uncover the optimal printing parameters for a specific bioink that result in high accuracy and precision. To test this hypothesis, we have implemented a FIS model consisting of four inputs (bioink concentration, printing flowrate, speed, and temperature) and two outputs to quantify the precision (scaffold bioprinted linewidth variance) and printability. We validate our use of the bioprinting precision index (BPI) with both standard and normalized printability factors. Finally, we utilize optimized printing parameters to bioprint scaffolds containing up to 30 x 106 cells/mL with high printability and precision. In total, our results indicate that computational methods are a cost-efficient measure to improve the precision and robustness of extrusion 3D bioprinting with gelatin-based bioinks.

Three dimensional (bio)printing of blood vessels: from vascularized tissues to functional arteries

“…Newer and advanced bioinks capable of carrying multiple cells can increase the structural integrity and viability of the printed constructs. Lastly, to better optimize the various parameters used in the printing process and to increase the cell viability of the printed structures, the role of artificial intelligence (AI) and specifically machine learning (ML) can be explored [208]. AI can be used to correlate input parameters such as bioink density, viscosity, nozzle diameter, part geometries, and types of cells with output parameters such as cell viability, mechanical properties, and geometrical accuracies along the (bio)printing process chain.…”