A Design of Experiment to Evaluate the Printability for Bioprinting by Using Deep Learning Image Similarity

Abstract

Bioprinting is a growing area in the field of tissue engineering that offers a potential solution to the global shortage of organ transplants. Ensuring high printability is crucial for bioprinting. To better understand printability, a design of experiment model that examines printing speed and pressure in extrusion-based printing was developed. Two biomaterials, hyaluronic acid and sodium alginate, were selected as surrogate biomaterials to understand how rheological properties play a role in printability. Various rheological aspects such as shear-thinning behavior, viscosity, and recovery were investigated. To further evaluate printability, a new method was used that includes deep learning image similarity. The information obtained with the surrogate bioinks was then applied to another biomaterial, methacrylated hyaluronic acid, in combination with corneal keratocytes to demonstrate the successful implementation of the outcome of this design of experiment. As a result of this study, a better understanding of the rheological properties for bioprinting was achieved, leading to a next step towards improving extrusion-based bioprinting, which can be used for a wide range of applications.