Prediction of cell viability in dynamic optical projection stereolithography-based bioprinting using machine learning
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Prediction of cell viability in dynamic optical projection stereolithography-based bioprinting using machine learning Heqi Xu1 · Qingyang Liu2 · Jazzmin Casillas3 · Mei Mcanally1 · Noshin Mubtasim4 · Lauren S. Gollahon4 · Dazhong Wu2 · Changxue Xu1 Received: 2 August 2020 / Accepted: 29 October 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
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
Introduction
Heqi Xu and Qingyang Liu have contributed equally to this study
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Dazhong Wu [email protected] Changxue Xu [email protected] Heqi Xu [email protected] Qingyang Liu [email protected] Jazzmin Casillas [email protected] Mei Mcanally [email protected] Noshin Mubtasim [email protected] Lauren S. Gollahon [email protected]
Three dimensional (3D) bioprinting techniques can fabricate functional tissues with biocompatible materials and living cells. Several representative biomimetic tissues and organs such as blood vessels, skins, bones, and cartilages have been fabricated with various 3D bioprinting techniques (Mandrycky et al. 2016). Two bioprinting mechanisms have been developed: scaffold-based and scaffold-free bioprinting. During scaffold-based bioprinting, cells are seeded on a 1
Department of Industrial, Manufacturing, and Systems Engineering, Texas Tech University, Lubbock, TX 79409, USA
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Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL 32816, USA
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Department of Mechanical Engineering, Texas Tech University, Lubbock, TX 79409, USA
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