A practical machine learning approach for predicting the quality of 3D (bio)printed scaffolds

Abstract 3D (Bio)printing is a highly effective method for fabricating tissue engineering scaffolds, renowned for their exceptional precision and control. Artificial intelligence (AI) has become a crucial technology in this field, capable of learning and replicating complex patterns that surpass human capabilities. However, the integration of AI in tissue engineering is often hampered by the lack of comprehensive and reliable data. This study addresses these challenges by providing one of the most extensive datasets on 3D-printed scaffolds. It provides the most comprehensive open-source dataset and employs various AI techniques, from unsupervised to supervised learning. This dataset includes detailed information on 1,171 scaffolds, featuring a variety of biomaterials and concentrations—including 60 biomaterials such as natural and synthesized biomaterials, crosslinkers, enzymes, etc.—along with 49 cell lines, cell densities, and different printing conditions. We used over 40 machine learning and deep learning algorithms, tuning their hyperparameters to reveal hidden patterns and predict cell response, printability, and scaffold quality. The clustering analysis using KMeans identified five distinct ones. In classification tasks, algorithms such as XGBoost, Gradient Boosting, Extra Trees Classifier, Random Forest Classifier, and LightGBM demonstrated superior performance, achieving higher accuracy and F1 scores. A fully connected neural network with six hidden layers from scratch was developed, precisely tuning its hyperparameters for accurate predictions. To promote future research, the developed dataset and the associated code are publicly available on www.github.com/saeedrafieyan/MLATE.