Approximating Scaffold Printability Utilizing Computational Methods
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) across samples by optimizing bioink-specific printing parameters. Specifically, we hypothesized that an adaptive neuro-fuzzy inference system (ANFIS) 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 an ANFIS model consisting of four inputs (bioink concentration, printing pressure, speed, and temperature) and a single output to quantify the precision (scaffold bioprinted linewidth range). We validate our use of the bioprinting precision index (BPI) with both standard and normalized printability factors. 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.