Machine Assisted Experimentation of Extrusion-Based Bioprinting Systems.

ABSTRACT: Optimization of extrusion-based bioprinting (EBB) parameters have been systematically conducted through experimentation. However, the process is time- and resource-intensive and not easily translatable to other laboratories. This study approaches EBB parameter optimization through machine learning (ML) models trained using data collected from the published literature. We investigated regression-based and classification-based ML models and their abilities to predict printing outcomes of cell viability and filament diameter for cell-containing alginate and gelatin composite bioinks. In addition, we interrogated if regression-based models can predict suitable extrusion pressure given the desired cell viability when keeping other experimental parameters constant. We also compared models traine

SUBMITTER: Tian S 

PROVIDER: S-EPMC8305959 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature