Machine learning boosts three-dimensional bioprinting

Ning, Hongwei; Zhou, Teng; Joo, Sang Woo

doi:10.18063/ijb.739

Cited by 15 publications

(3 citation statements)

References 89 publications

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“…Usually, identification of the ideal printing parameters that would ensure both good printability and low cell mortality can be very lackluster, requiring multiple trial and error experiments which cost both money and time. Data-driven machine learning algorithms can impactfully hasten the investigation process for the ideal bioink traits, by delving into vast data collections, accruing particular information based on the desirable properties, and finally conferring an adept predictive model, even before commencing any further in vitro experiments [ 311 , 312 ].…”

Section: Applications Of Bioprinting In Tissue and Organ Regenerationmentioning

confidence: 99%

Advances in 3D bioprinting for regenerative medicine applications

Loukelis,

Koutsomarkos,

Mikos

et al. 2024

Regenerative Biomaterials

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Biofabrication techniques allow for the construction of biocompatible and biofunctional structures composed from biomaterials, cells and biomolecules. Bioprinting is an emerging 3D printing method which utilizes biomaterial-based mixtures with cells and other biological constituents into printable suspensions known as bioinks. Coupled with automated design protocols and based on different modes for droplet deposition, 3D bioprinters are able to fabricate hydrogel-based objects with specific architecture and geometrical properties, providing the necessary environment that promotes cell growth and directs cell differentiation towards application-related lineages. For the preparation of such bioinks, various water-soluble biomaterials have been employed, including natural and synthetic biopolymers, and inorganic materials. Bioprinted constructs are considered to be one of the most promising avenues in regenerative medicine due to their native organ biomimicry. For successful application, the bioprinted constructs should meet particular criteria such as optimal biological response, mechanical properties similar to the target tissue, high levels of reproducibility and printing fidelity, but also increased upscaling capability. In this review, we highlight the most recent advances in bioprinting, focusing on the regeneration of various tissues including bone, cartilage, cardiovascular, neural, skin, and other organs such as liver, kidney, pancreas, and lungs. We discuss the rapidly developing co-culture bioprinting systems used to resemble the complexity of tissues and organs and the crosstalk between various cell populations towards regeneration. Moreover, we report on the basic physical principles governing 3D bioprinting, and the ideal bioink properties based on the biomaterials’ regenerative potential. We examine and critically discuss the present status of 3D bioprinting regarding its applicability and current limitations that need to be overcome to establish it at the forefront of artificial organ production and transplantation.

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Section: Applications Of Bioprinting In Tissue and Organ Regenerationmentioning

confidence: 99%

Advances in 3D bioprinting for regenerative medicine applications

Loukelis,

Koutsomarkos,

Mikos

et al. 2024

Regenerative Biomaterials

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“…In the context of 3D-printed hydrogels, CNNs can analyse the printing process and optimise printing parameters. By learning from 3D printing data, CNNs help the user adjust the printing speed, material deposition rate, and other variables to achieve the desired printing outcomes [95]. For example, a deep learning model using CNN was used to generate a model that differentiated between excellent and poor hydrogel prints.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Exploring the Potential of Artificial Intelligence for Hydrogel Development—A Short Review

Negut,

Bita

2023

Gels

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AI and ML have emerged as transformative tools in various scientific domains, including hydrogel design. This work explores the integration of AI and ML techniques in the realm of hydrogel development, highlighting their significance in enhancing the design, characterisation, and optimisation of hydrogels for diverse applications. We introduced the concept of AI train hydrogel design, underscoring its potential to decode intricate relationships between hydrogel compositions, structures, and properties from complex data sets. In this work, we outlined classical physical and chemical techniques in hydrogel design, setting the stage for AI/ML advancements. These methods provide a foundational understanding for the subsequent AI-driven innovations. Numerical and analytical methods empowered by AI/ML were also included. These computational tools enable predictive simulations of hydrogel behaviour under varying conditions, aiding in property customisation. We also emphasised AI’s impact, elucidating its role in rapid material discovery, precise property predictions, and optimal design. ML techniques like neural networks and support vector machines that expedite pattern recognition and predictive modelling using vast datasets, advancing hydrogel formulation discovery are also presented. AI and ML’s have a transformative influence on hydrogel design. AI and ML have revolutionised hydrogel design by expediting material discovery, optimising properties, reducing costs, and enabling precise customisation. These technologies have the potential to address pressing healthcare and biomedical challenges, offering innovative solutions for drug delivery, tissue engineering, wound healing, and more. By harmonising computational insights with classical techniques, researchers can unlock unprecedented hydrogel potentials, tailoring solutions for diverse applications.

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“…Recent studies have shown successful applications of machine learning techniques to enhance the 3D printing of biomaterials [11][12][13][14][15]. In these studies, ML has been employed for three main purposes: 1.…”

Section: Introductionmentioning

confidence: 99%

Open-Loop Control System for High Precision Extrusion-Based Bioprinting Through Machine Learning Modeling

Arduengo,

Hascoet,

Chinesta

et al. 2024

Journal of Machine Engineering

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Bioprinting is a process that uses 3D printing techniques to combine cells, growth factors, and biomaterials to create biomedical components, often with the aim of imitating natural tissue characteristics. Typically, 3D bioprinting adopts a layer-by-layer method, using materials known as bio-inks to build structures resembling tissues. This study introduces an open-loop control system designed to improve the accuracy of extrusion-based bioprinting techniques, which is composed of a specific experimental setup and a series of algorithms and models. Firstly, a method employing Logistic Regression is used to select the tests that will serve to train and test the following model. Then, using a Machine Learning Algorithm, a model that allows the optimization of printing parameters and enables process control through an open-loop system was developed. Through rigorous experimentation and validation, it is shown that the model exhibits a high degree of accuracy in independent tests. Thus, the control system offers predictability and adaptability capabilities to ensure the consistent production of high-quality bioprinted structures. Experimental results confirm the efficacy of this machine learning model and the open-loop control system in achieving optimal bioprinting outcomes.

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Machine learning boosts three-dimensional bioprinting

Cited by 15 publications

References 89 publications

Advances in 3D bioprinting for regenerative medicine applications

Advances in 3D bioprinting for regenerative medicine applications

Exploring the Potential of Artificial Intelligence for Hydrogel Development—A Short Review

Open-Loop Control System for High Precision Extrusion-Based Bioprinting Through Machine Learning Modeling

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