Machine Learning in Predicting Printable Biomaterial Formulations for Direct Ink Writing.

ABSTRACT: Three-dimensional (3D) printing is emerging as a transformative technology for biomedical engineering. The 3D printed product can be patient-specific by allowing customizability and direct control of the architecture. The trial-and-error approach currently used for developing the composition of printable inks is time- and resource-consuming due to the increasing number of variables requiring expert knowledge. Artificial intelligence has the potential to reshape the ink development process by forming a predictive model for printability from experimental data. In this paper, we constructed machine learning (ML) algorithms including decision tree, random forest (RF), and deep learning (DL) to predict the printability of biomaterials. A total of 210 formulations including 16 different bioactiv

SUBMITTER: Chen H 

PROVIDER: S-EPMC10353544 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature