Machine Learning-Guided Three-Dimensional Printing of Tissue Engineering Scaffolds

• Published in: Tissue engineering. Part A Vol. 26; no. 23-24; p. 1359
• Main Authors: Conev, Anja, Litsa, Eleni E, Perez, Marissa R, Diba, Mani, Mikos, Antonios G, Kavraki, Lydia E
• Format: Journal Article
• Language: English
• Published: United States 01.12.2020
• Subjects: Machine Learning; Porosity; Printing, Three-Dimensional; Tissue Engineering; Tissue Scaffolds; random forests; tissue engineering; biomaterials; machine learning; printing quality prediction; 3D printing
• DOI: 10.1089/ten.tea.2020.0191

Various material compositions have been successfully used in 3D printing with promising applications as scaffolds in tissue engineering. However, identifying suitable printing conditions for new materials requires extensive experimentation in a time and resource-demanding process. This study investigates the use of Machine Learning (ML) for distinguishing between printing configurations that are likely to result in low-quality prints and printing configurations that are more promising as a first step toward the development of a recommendation system for identifying suitable printing conditions. The ML-based framework takes as input the printing conditions regarding the material composition and the printing parameters and predicts the quality of the resulting print as either "low" or "high." We investigate two ML-based approaches: a direct classification-based approach that trains a classifier to distinguish between low- and high-quality prints and an indirect approach that uses a regression ML model that approximates the values of a printing quality metric. Both modes are built upon Random Forests. We trained and evaluated the models on a dataset that was generated in a previous study, which investigated fabrication of porous polymer scaffolds by means of extrusion-based 3D printing with a full-factorial design. Our results show that both models were able to correctly label the majority of the tested configurations while a simpler linear ML model was not effective. Additionally, our analysis showed that a full factorial design for data collection can lead to redundancies in the data, in the context of ML, and we propose a more efficient data collection strategy.