TY - JOUR
T1 - Prediction of cell viability in dynamic optical projection stereolithography-based bioprinting using machine learning
AU - Xu, Heqi
AU - Liu, Qingyang
AU - Casillas, Jazzmin
AU - Mcanally, Mei
AU - Mubtasim, Noshin
AU - Gollahon, Lauren S.
AU - Wu, Dazhong
AU - Xu, Changxue
N1 - Publisher Copyright:
© 2020, Springer Science+Business Media, LLC, part of Springer Nature.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Bioprinting
KW - Cell viability
KW - Dynamic optical projection stereolithography
KW - Machine learning
KW - Predictive modeling
UR - http://www.scopus.com/inward/record.url?scp=85096077951&partnerID=8YFLogxK
U2 - 10.1007/s10845-020-01708-5
DO - 10.1007/s10845-020-01708-5
M3 - Article
AN - SCOPUS:85096077951
JO - Journal of Intelligent Manufacturing
JF - Journal of Intelligent Manufacturing
SN - 0956-5515
ER -