The Role of Machine Learning and Design of Experiments in the Advancement of Biomaterial and Tissue Engineering Research

Abstract

Optimisation of tissue engineering (TE) processes requires models that can identify relationships between the parameters to be optimised and predict structural and performance outcomes from both physical and chemical processes. Currently, Design of Experiments (DoE) methods are commonly used for optimisation purposes in addition to playing an important role in statistical quality control and systematic randomisation for experiment planning. DoE is only used for the analysis and optimisation of quantitative data (i.e., number-based, countable or measurable), while it lacks the suitability for imaging and high dimensional data analysis. Machine learning (ML) offers considerable potential for data analysis, providing a greater flexibility in terms of data that can be used for optimisation and predictions. Its application within the fields of biomaterials and TE has recently been explored. This review presents the different types of DoE methodologies and the appropriate methods that have been used in TE applications. Next, ML algorithms that are widely used for optimisation and predictions are introduced and their advantages and disadvantages are presented. The use of different ML algorithms for TE applications is reviewed, with a particular focus on their use in optimising 3D bioprinting processes for tissue-engineered construct fabrication. Finally, the review discusses the future perspectives and presents the possibility of integrating DoE and ML in one system that would provide opportunities for researchers to achieve greater improvements in the TE field.

3D-printing of porous structures for reproduction of a femoral bone

“…47 Another future development concerns the improvement of automation for the reconstruction phase to have an increasingly accurate as well as fast method. 48…”

Shape Fidelity Evaluation of Alginate-Based Hydrogels through Extrusion-Based Bioprinting

“…Considering the accessibility and repeatability, these proposed methods should prove highly suitable for bioink development and help accelerate the process. Future studies can further focus on automized monitoring and optimization of the 3D-printing process (e.g., using machine learning techniques) to be able to detect defects in real time and apply required adjustments in printing parameters [ 1 , 55 , 56 , 57 , 58 ]. Future studies can be performed encompassing not only rheological challenges associated with the mixtures of various alginate bioinks but also the effects of environmental stimuli (e.g., the evaporation of the material, dynamic surface tension, and Marangoni stresses due to variation in boundary conditions).…”

Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future

“…Optimal conditions for biochemical production are effectively identified through ML, facilitating increased yields and process efficiency. 69,70,182 Enhanced efficiency conserves resources and substantially reduces production time and costs. ML's influence transcends traditional boundaries, imbuing the biochemical realm with unprecedented precision and control.…”

“…In the sector of renewable energy systems, a remarkable enhancement is noticed in the integration of biobased polymers, courtesy of ML interventions. , Renewable energy apparatuses like solar cells and wind turbines are experiencing a boost in operational efficiency by incorporating ML-optimized biobased polymers. , By understanding the intricate material properties and performance characteristics, ML aids in customizing biobased polymers to complement the unique requirements of different renewable energy systems . This cooperation between ML and biobased polymers is proving to be instrumental in augmenting the sustainability and efficiency of renewable energy systems, contributing significantly to global sustainable energy goals.…”

“…This data management allows for the identification of patterns and trends that would be otherwise obscure, granting an enhanced understanding of diverse biochemical processes. Optimal conditions for biochemical production are effectively identified through ML, facilitating increased yields and process efficiency. ,, Enhanced efficiency conserves resources and substantially reduces production time and costs. ML’s influence transcends traditional boundaries, imbuing the biochemical realm with unprecedented precision and control.…”