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Published Online: 16 December 2020

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

Publication: Tissue Engineering Part A
Volume 26, Issue Number 23-24


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.


Impact statement

This study investigates the use of Machine Learning (ML) for predicting the printing quality given the printing conditions in extrusion-based 3D printing of biomaterials. Classification and regression methods built upon Random Forests show promise for the development of a recommendation system for identifying suitable printing conditions reducing the amount of required experimentation. This study also gives insights on developing an efficient strategy for collecting data for training ML models for predicting printing quality in extrusion-based 3D printing of biomaterials.

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Information & Authors


Published In

cover image Tissue Engineering Part A
Tissue Engineering Part A
Volume 26Issue Number 23-24December 2020
Pages: 1359 - 1368
PubMed: 32940144


Published online: 16 December 2020
Published in print: December 2020
Published ahead of print: 15 October 2020
Published ahead of production: 17 September 2020
Accepted: 14 September 2020
Received: 8 July 2020


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Anja Conev*
Department of Computer Science and Rice University, Houston, Texas, USA.
Eleni E. Litsa*
Department of Computer Science and Rice University, Houston, Texas, USA.
Marissa R. Perez
Department of Bioengineering, Rice University, Houston, Texas, USA.
NIH/NIBIB Center for Engineering Complex Tissues, USA.
Mani Diba
Department of Bioengineering, Rice University, Houston, Texas, USA.
NIH/NIBIB Center for Engineering Complex Tissues, USA.
Antonios G. Mikos
Department of Bioengineering, Rice University, Houston, Texas, USA.
NIH/NIBIB Center for Engineering Complex Tissues, USA.
Lydia E. Kavraki [email protected]
Department of Computer Science and Rice University, Houston, Texas, USA.


These authors contributed equally to this work.
Address correspondence to: Lydia E. Kavraki, PhD, Department of Computer Science, MS 132, Rice University, 6100 Main Street, Houston, TX 77005, USA [email protected]

Disclosure Statement

No competing financial interests exist.

Funding Information

We acknowledge support toward 3D printing of tissue engineering scaffolds from the National Institutes of Health (P41 EB023833) (AGM) and Rice University Funds (LEK). We also acknowledge support from a National Science Foundation Graduate Research Fellowship (MRP) and a Rubicon postdoctoral fellowship from the Netherlands Organization for Scientific Research (Project No. 019.182EN.004) (MD).

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