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.

基于立体光刻（SLA）的生物打印可以准确，高效地制作三维复杂物体。但是，基于SLA的生物打印过程中的紫外线（UV）照射是一项重大挑战，可能会损坏细胞。由于细胞生物学结构和细胞恢复的复杂性，基于物理的模型无法高精度地预测细胞活力。为了克服这一挑战，我们使用机器学习开发了一种预测模型来预测细胞活力。我们设计了一组实验，其中考虑了四个关键工艺参数的影响，这些参数包括UV强度，UV暴露时间，甲基丙烯酸明胶浓度和层厚度。这些实验是在不同的生物打印条件下进行的，以收集实验数据。结合神经网络，岭回归，K近邻和随机森林（RF）的集成学习算法被开发出来，旨在预测各种生物打印条件下的细胞活力。基于三个误差指标评估了预测模型的性能。最后，使用RF确定每个工艺参数对细胞活力的重要性。已经证明该预测模型能够以高精度预测细胞生存力，并能够确定基于SLA的3D生物打印中每个过程参数对细胞生存力的重要性。使用RF确定每个工艺参数对细胞活力的重要性。预测模型已被证明能够高精度地预测细胞生存力，并能够确定基于SLA的3D生物打印中每个过程参数对细胞生存力的重要性。使用RF确定每个工艺参数对细胞活力的重要性。已经证明该预测模型能够以高精度预测细胞生存力，并能够确定基于SLA的3D生物打印中每个过程参数对细胞生存力的重要性。