The Research for Quality Improvement of Bio-Scaffold for Tissue Engineering Applications by Using Deep Learning

Project Details


The purpose of this project is to improve the appearance quality of the bio-scaffold by utilizing deep learning. The materials of bio-scaffolds can be roughly divided into two categories: natural and synthetic materials. The most widely used natural materials are collagen, sodium alginate, gelatin and chitosan. As these natural materials are obtained from animals and plants, there is always a slight difference between the bio-inks in each batch. Most of the fabrication parameters have dependence with each other, it is difficult to derive effective predictive formulas for manufacturing scaffolds. Often can only use the trial and error method, resulting in the need to spend a lot of time to adjust the fabrication parameters for achieving the preset target. Multilayer perceptron is a kind of Deep learning, and it can train a model through a large amount of historical data, and predictable results obtain after inputting new data into the model. The capability can help to improve the appearance quality of the bio-scaffold.The project is planned for a three-year period, with the first year focusing on the construction of deep learning software and hardware, as well as, various types of sensor signals from bio-printing system will connect to deep learning system, and then classify and regularize. These regularized data will extract into feature for deep learning. The second year will focus on the development for online automatic measurement of scaffold shape, as the labels in depth learning. This work will take together with the results from first year, and bring an intelligent bio-printing system that automatically collects training data and automates training. The project plans to use chitosan, gelatin and sodium alginate as scaffold materials, and to produce a large number of scaffolds in the third year. The actual dimensions of the scaffold fabricated will compare with the predicted results, and the model of the deep learning will revise and improve. Through this three year project, this project would develop a bio-printing system capable of producing high quality scaffolds using natural materials.
Effective start/end date1/08/1831/07/19

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 4 - Quality Education
  • SDG 15 - Life on Land
  • SDG 17 - Partnerships for the Goals


  • Bio-printing
  • Deep Learning
  • Additive Manufacturing
  • Multilayer Perceptron
  • Machine Learning


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