Inverse Design of Scaffolds for Bone Tissue Engineering using Artificial Neural Networks and Generative Additive Models

Scaffold design for bone tissue engineering along with recent advances in 3D printing represents a major technology to promote the healing of critical bone defects. However, designing the scaffold geometry from a given set of desired properties is challenging and typically tackled by means of high-complexity finite element simulations. Recently, the use of artificial neural networks (ANNs) has started emerging in this field and has provided promising results. In this study, we investigated the development of an ANN to predict the design parameters of a specific geometrical structure from the family of Triply-Periodic Minimal Surfaces, i.e., the gyroid. Unlike other studies, we investigated i) the possibility of using only morphological characteristics of the scaffold to predict the design parameters; ii) the prediction of anisotropic scaffolds since anisotropy has been found to significantly improve bone regeneration; and iii) the comparison of the performance of the ANN with a generalized additive model (GAM) previously designed for the same task. We generated a synthetic dataset of 6940 gyroids where the 90% was used to train the models and 10% for performance evaluation. A feature selection procedure was implemented to select the optimal feature set for the prediction. With the same feature set, the ANN outperformed the GAM in the prediction of all design parameters with Pearson's correlation coefficients ranging from 0.53 to 0.82, while the GAM's ranged from 0.41 to 0.51. The ANN also displayed a lower mean absolute error than GAM. The results of the study support the use of ANN for scaffold design. Further evaluations are needed to assess the feasibility of this technology in clinical applications.