Open AccessMachine Learning Techniques in Structure-Property Optimization of Polymeric Scaffolds for Tissue Engineering
Author(s) -
Zigeng Wang,
Xiangming Xiao,
Syam P. Nukavarapu,
Sangamesh Kumbar,
Sanguthevar Rajasekaran
Publication year - 2022
Publication title -
epic series in computing
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.21
H-Index - 7
ISSN - 2398-7340
DOI - 10.29007/nxm3
Subject(s) - biomaterial , computer science , property (philosophy) , tissue engineering , scaffold , process (computing) , mechatronics , electrospinning , materials science , mechanical engineering , biomedical engineering , nanotechnology , engineering , artificial intelligence , polymer , composite material , philosophy , epistemology , operating system
Biomaterials and biomedical implants have revolutionized the way medicine is practiced. Technologies, such as 3D printing and electrospinning, are currently employed to create novel biomaterials. Most of the synthesis techniques are ad-hoc, time taking, and expensive. These shortcomings can be overcome greatly with the employment of computational techniques. In this paper we consider the problem of bone tissue engineering as an example and show the potentials of machine learning approaches in biomaterial construction, in which different models was built to predict the elastic modulus of the scaffold at given an arbitrary material composition. Likewise, the methodology was extended to cell-material interaction and prediction at an arbitrary process parameter.