Machine learning-driven prediction of gel fraction in conductive gelatin methacryloyl hydrogels
Journal Title: International Journal of AI for Materials and Design - Year 2024, Vol 1, Issue 2
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.
Authors and Affiliations
Xi Huang|1* , Ye Xuan Wong|1 , Guo Liang Goh|1 , Xinchao Gao|1 , Jia Min Lee|1 , Wai Yee Yeong|1* 1 School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
Keywords
Related Articles
- EP ID EP767462
- DOI 10.36922/ijamd.3807
- Views 4
- Downloads 0
Integration of physics-based data in deep learning model training for predicting the effect of sulfur content in the directed energy deposition process
The training of a machine learning model solely on experimental data, encompassing both pre- and post-process information, can reveal the general relationship of the directed energy deposition process. However, models tr...
Machine learning techniques for quality assurance in additive manufacturing processes
Additive manufacturing (AM) processes have revolutionized manufacturing industries by enabling the production of complex geometries with reduced material waste and lead times. However, ensuring the quality of AM parts re...
A unified industrial large knowledge model framework in Industry 4.0 and smart manufacturing
The recent emergence of large language models (LLMs) demonstrates the potential for artificial general intelligence, revealing new opportunities in Industry 4.0 and smart manufacturing. However, a notable gap exists in a...
Machine learning applications for quality improvement in laser powder bed fusion: A state-of-the-art review
As one of the most popular additive manufacturing methods, laser powder bed fusion (L-PBF) builds 3D components with complex geometries layer by layer using alloy powders. This technique has found widespread adoption in...
Manufacturing multi-organs database: A comprehensive, predictive, and analytical biofabrication database
Biofabrication, broadly described as “a process that results in a defined product with biological function,” has undergone a revolution in recent years. This revolution has led to an explosion of literature containing va...