Author ORCID Identifier


Defense Date


Document Type


Degree Name

Master of Science


Chemical and Life Science Engineering

First Advisor

Nastassja Lewinski

Second Advisor

Bridget McInnes

Third Advisor

Christina Tang


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.


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