DOI
https://doi.org/10.25772/A2WF-Q973
Author ORCID Identifier
0000-0001-6444-651X
Defense Date
2021
Document Type
Thesis
Degree Name
Master of Science
Department
Chemical and Life Science Engineering
First Advisor
Nastassja Lewinski
Second Advisor
Bridget McInnes
Third Advisor
Christina Tang
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 across different laboratories. A machine learning (ML) approach to EBB parameter optimization can accelerate this process for laboratories across the field through training using data collected from published literature. In this work, regression-based and classification-based ML models were investigated for their abilities to predict printing outcomes of cell viability and filament diameter for cell-containing alginate and gelatin composite hydrogels. Regression-based models were investigated for their ability to predict suitable extrusion pressure given desired cell viability when keeping other experimental parameters constant. Also, models trained across data from general literature were compared to models trained across data from one literature source that utilized alginate and gelatin bioinks and experimental conditions closely replicatable with available laboratory resources. The results indicate that models trained on large amounts of generalized data can impart physical trends on cell viability, filament diameter, and extrusion pressure seen in past literature. Regression models trained on the larger dataset also predicted cell viability closer to experimental values for material concentration combinations not seen in training data of the single-paper-based regression models. While the best performing classification models for cell viability can achieve an average prediction accuracy of around 70%, the cell viability predictions remained constant despite altering input parameter combinations. Trained models on bioprinting literature data show the potential usage of applying ML models to bioprinting experimental design.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
12-16-2021
Included in
Artificial Intelligence and Robotics Commons, Biomaterials Commons, Biomedical Devices and Instrumentation Commons, Polymer and Organic Materials Commons, Polymer Science Commons