Machine Learning approaches for the design of biomechanically compatible bone tissue engineering scaffolds

Abstract

Triply-Periodic Minimal Surfaces (TPMS) analytical formulation does not provide a direct correlation between the input parameters (analytical) and the mechanical and morphological properties of the structure. In this work, we created a dataset with more than one thousand TPMS scaffolds for the training of Machine Learning (ML) models able to find such correlation. Finite Element Modeling and image analysis have been used to characterize the scaffolds. In particular, we trained three different ML models, exploring both a linear and non-linear approach, to select the features able to predict the input parameters. Furthermore, the features used for the prediction can be selected in three different modes: i) fully automatic, through a greedy algorithm, ii) arbitrarily, by the user and iii) in a combination of the two above methods: i.e. partially automatic and partially through a user-selection. The latter, coupled with the non-linear ML model, exhibits a median error less than 3% and a determination coefficient higher than 0.89 for each of the selected features, and all of them are accessible during the design phase. This approach has been applied to the design of a hydroxyapatite TPMS scaffolds with prescribed properties obtained from a real trabecular-like hydroxyapatite scaffold. The obtained results demonstrate that the ML model can effectively design a TPMS scaffold with prescribed features on the basis of biomechanical, mechanobiology and technological constraints.