AccScience Publishing / IJB / Volume 9 / Issue 6 / DOI: 10.36922/ijb.1280

Rheology-informed hierarchical machine learning model for the prediction of printing resolution in extrusion-based bioprinting

Dageon Oh1 Masoud Shirzad1 Min Chang Kim2 Eun-Jae Chung3 Seung Yun Nam1,2*
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1 Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan 48513, Republic of Korea
2 Major of Biomedical Engineering, Division of Smart Healthcare, Pukyong National University, Busan 48513, Republic of Korea
3 Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
Submitted: 16 March 2023 | Accepted: 13 July 2023 | Published: 9 August 2023
© 2023 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( )

In this study, a rheology-informed hierarchical machine learning (RIHML) model was developed to improve the prediction accuracy of the printing resolution of constructs fabricated by extrusion-based bioprinting. Specifically, the RIHML model, as well as conventional models such as the concentration-dependent model and printing parameter-dependent model, was trained and tested using a small dataset of bioink properties and printing parameters. Interestingly, the results showed that the RIHML model exhibited the lowest error percentage in predicting the printing resolution for different printing parameters such as nozzle velocities and pressures, as well as for different concentrations of the bioink constituents. Besides, the RIHML model could predict the printing resolution with reasonably low errors even when using a new material added to the alginate-based bioink, which is a challenging task for conventional models. Overall, the results indicate that the RIHML model can be a useful tool to predict the printing resolution of extrusion-based bioprinting, and it is versatile and expandable compared to conventional models since the RIHML model can easily generalize and embrace new data.

Machine learning
Printing resolution
This research was supported by a National Research Foundation of Korea (NRF) grant (NRF- 2021R1I1A3040459) funded by the Korean government (MOE). This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number : HI22C1323).
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Conflict of interest
The authors declare no conflicts of interests.
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International Journal of Bioprinting, Electronic ISSN: 2424-8002 Print ISSN: 2424-7723, Published by AccScience Publishing