Prediction of cell viability in dynamic optical projection stereolithography-based bioprinting using machine learning

Heqi Xu (), Qingyang Liu (), Jazzmin Casillas (), Mei Mcanally (), Noshin Mubtasim (), Lauren S. Gollahon (), Dazhong Wu () and Changxue Xu ()
Additional contact information
Heqi Xu: Texas Tech University
Qingyang Liu: University of Central Florida
Jazzmin Casillas: Texas Tech University
Mei Mcanally: Texas Tech University
Noshin Mubtasim: Texas Tech University
Lauren S. Gollahon: Texas Tech University
Dazhong Wu: University of Central Florida
Changxue Xu: Texas Tech University

Journal of Intelligent Manufacturing, 2022, vol. 33, issue 4, No 7, 995-1005

Abstract: Abstract Stereolithography (SLA)-based bioprinting can fabricate three-dimensional complex objects accurately and efficiently. However, the ultraviolet (UV) irradiation in the SLA-based bioprinting process is a significant challenge, which may damage the cells. Physics-based models are not able to predict cell viability with high accuracy because of the complexity of cell biological structures and cell recovery. To overcome this challenge, we developed a predictive model using machine learning to predict cell viability. We designed a set of experiments considering the effects of four critical process parameters, including UV intensity, UV exposure time, gelatin methacrylate concentration, and layer thickness. These experiments were conducted under varying bioprinting conditions to collect experimental data. An ensemble learning algorithm combining neural networks, ridge regression, K-nearest neighbors, and random forest (RF) was developed aiming at predicting cell viability under various bioprinting conditions. The performance of the predictive model was evaluated based on three error metrics. Finally, the importance of each process parameter on cell viability was determined using RF. The predictive model has been demonstrated to be able to predict cell viability with high accuracy as well as determine the significance of each process parameter on cell viability in SLA-based 3D bioprinting.

Keywords: Bioprinting; Dynamic optical projection stereolithography; Cell viability; Predictive modeling; Machine learning (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-020-01708-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:33:y:2022:i:4:d:10.1007_s10845-020-01708-5

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-020-01708-5

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().