AccScience Publishing / IJB / Online First / DOI: 10.36922/ijb.6701
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RESEARCH ARTICLE

An ink-insensitive deep learning model for improving the printing quality in extrusion-based bioprinting

Wei-Chih Tseng1,2 ,  Chao-Yaug Liao2* ,  Luc Chassagne1 ,  Barthélemy Cagneau1*
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1 Laboratoire d’Ingénierie des Systèmes de Versailles (LISV), UVSQ - Université Paris-Saclay, Vélizy-Villacoublay, France
2 Department of Mechanical Engineering, College of Engineering, National Central University, Taoyuan, Taiwan
IJB, 6701 https://doi.org/10.36922/ijb.6701
Submitted: 29 November 2024 | Accepted: 18 December 2024 | Published: 18 December 2024
© 2024 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 ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Extrusion-based bioprinting (EBB) is an efficient, simple, and cost-effective bioprinting technique. However, due to the high complexity of biomaterial inks, a trial-and-error approach is typically required for determining the optimal printing parameter configurations. Thus, the quality and reproducibility of printed scaffolds remain a concern. In this study, we integrated flow sensing into the EBB and replaced conventional fixed-value printing parameters by capturing the time-series data of all printing parameters to enhance the monitoring of the printing process. To improve the EBB adaptability, we conducted experiments with three biomaterial inks with distinct properties under various parameter configurations to achieve ink-insensitive linewidth prediction through the construction and assessment of deep learning models. Additionally, two explainable artificial intelligence methods were used to analyze the decision-making process of the deep learning model. This analysis not only enhanced model reliability but also identified key features in printing parameters and time-series data. These results can be used to improve the efficiency and quality of the EBB processes by using various biomaterial inks.

Graphical abstract
Keywords
Deep learning
Explainable artificial intelligence
Extrusion-based bioprinting
Flow rate measurement
Printing quality
Time-series
Funding
This work was supported in part by the National Science and Technology Council, Taiwan, under Grant NSTC 112-2221-E-008-021, and in part by the French National Research Agency (ANR) as part of the “Investissements d’Avenir” program, through the IDEX Paris-Saclay, under Grant ANR-11-IDEX-0003-02.
Conflict of interest
The authors declare they have no competing interests.
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International Journal of Bioprinting, Electronic ISSN: 2424-8002 Print ISSN: 2424-7723, Published by AccScience Publishing
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