AccScience Publishing / IJAMD / Volume 1 / Issue 2 / DOI: 10.36922/ijamd.3807
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ORIGINAL RESEARCH ARTICLE

Machine learning-driven prediction of gel fraction in conductive gelatin methacryloyl hydrogels

Xi Huang1* ,  Ye Xuan Wong1 ,  Guo Liang Goh1 ,  Xinchao Gao1 ,  Jia Min Lee1 ,  Wai Yee Yeong1*
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1 School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
IJAMD 2024, 1(2), 61–75; https://doi.org/10.36922/ijamd.3807
Submitted: 31 May 2024 | Accepted: 12 July 2024 | Published: 8 August 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

Gelatin methacryloyl (GelMA) hydrogels, combined with conductive fillers like Poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:SPSS), present significant promise for tissue regeneration due to their biocompatibility, biodegradability, and electrical conductivity. However, optimizing the curing process of the hydrogel is challenging due to a lack of an existing model for gel fraction prediction. This complexity is further heightened when additional variables such as bioink formulation and crosslinking parameters are considered. This study leverages machine learning (ML) to predict the gel fraction of GelMA-PEDOT:SPSS hydrogel based on the combination of three types of features: Bioink formulation, crosslinking parameters, and absorption coefficient. The two key objectives of this study are to develop an ML model to predict gel fraction from bioink formulation and crosslinking parameters such as ultraviolet (UV) power intensity and UV irradiation duration, and to create an ML model to predict gel fraction through the absorption coefficient instead of crosslinking parameter. In the first ML model, support vector regression achieved the highest accuracy with a mean absolute percentage error (MAPE) of 3.13% and an R² of 0.79. This model allows the user to select optimum bioink formulation and crosslinking parameters to achieve the required gel fraction with minimal experiment. For the second ML model that utilizes a combination of absorption coefficient and bioink formulation, deep neural network models achieved a MAPE of 6.31% and an R² of 0.54. The absorption coefficient model shows promise for a non-destructive, real-time assessment of gel fraction, enabling more precise control over the hydrogel properties during the curing process. These results demonstrate ML’s capability to efficiently optimize hydrogel formulations, significantly cut down experimental efforts, and improve precision in 3D bioprinting and other hydrogel applications, thereby advancing the field of tissue regeneration.

Keywords
3D bioprinting
3D printing
Biofabrication
Machine learning
Hydrogel
Composite
Funding
This research is supported by the National Research Foundation for NRF Investigatorship Award No.: NRF-NRFI07-2021-0007.
Conflict of interest
Wai Yee Yeong is an Editorial Board Member of this journal, but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Separately, other authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
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International Journal of AI for Materials and Design, Electronic ISSN: 3029-2573 Print ISSN: 3041-0746, Published by AccScience Publishing
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