Deep learning for fabrication and maturation of 3D bioprinted tissues and organs

Ng, Wei Long; Chan, Alvin; Ong, Yew Soon; Chua, Chee Kai

doi:10.1080/17452759.2020.1771741

Cited by 116 publications

(71 citation statements)

References 123 publications

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“…Over the years, progresses have been made in bioprinting technology in the aspects of material development, TE application, and software development. [ 105–112 ] There are three distinct types of bioprinting technologies, which are commonly used in TE application. [ 113 ] “Material extrusion” bioprinting deposits materials through a nozzle, producing defined structures with resolution determined by the i) flow rate ratio between the extrudate and printing platform, ii) the nozzle size, and iii) the height of the nozzle against print platform.…”

Section: Printing Collagenmentioning

confidence: 99%

Bioprinting of Collagen: Considerations, Potentials, and Applications

Lee

Suen

Ng³

et al. 2020

Macromolecular Bioscience

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Collagen is the most abundant extracellular matrix protein that is widely used in tissue engineering (TE). There is little research done on printing pure collagen. To understand the bottlenecks in printing pure collagen, it is imperative to understand collagen from a bottom‐up approach. Here it is aimed to provide a comprehensive overview of collagen printing, where collagen assembly in vivo and the various sources of collagen available for TE application are first understood. Next, the current printing technologies and strategy for printing collagen‐based materials are highlighted. Considerations and key challenges faced in collagen printing are identified. Finally, the key research areas that would enhance the functionality of printed collagen are presented.

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Section: Printing Collagenmentioning

confidence: 99%

Bioprinting of Collagen: Considerations, Potentials, and Applications

Lee

Suen

Ng³

et al. 2020

Macromolecular Bioscience

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“…In addition, various software programs are being developed to evaluate the tissue to be replaced, to create a 3D model that is as similar as possible, also building databases that allow the choice of the most suitable materials and processing conditions for it in each case [ 120 ]. Another interesting trend is related to the implementation of deep learning in the development of 3D bioprinted scaffolds, such as image-processing and segmentation, optimization and in-situ correction of printing parameters and lastly refinement of the tissue maturation process [ 121 ]. Thus, the biomaterial would specialize in the patient, improving their biocompatibility and adaptation.…”

Section: 3d Printingmentioning

confidence: 99%

Polymer-Based Scaffolds for Soft-Tissue Engineering

et al. 2020

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Biomaterials have been used since ancient times. However, it was not until the late 1960s when their development prospered, increasing the research on them. In recent years, the study of biomaterials has focused mainly on tissue regeneration, requiring a biomaterial that can support cells during their growth and fulfill the function of the replaced tissue until its regeneration. These materials, called scaffolds, have been developed with a wide variety of materials and processes, with the polymer ones being the most advanced. For this reason, the need arises for a review that compiles the techniques most used in the development of polymer-based scaffolds. This review has focused on three of the most used techniques: freeze-drying, electrospinning and 3D printing, focusing on current and future trends. In addition, the advantages and disadvantages of each of them have been compared.

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“…Recently, there is surge in scientific publications regarding the application of machine learning (ML) to bioprinting-relevant researches such as medical imaging and segmentation, optimization of bioinks or bioprinting process as well as in vitro parametric studies, which are well reviewed in Yu and Jiang[ 1 ], Ng et al . [ 2 ]. Both recent articles focused on the benefits and potential of ML but missed a clear portrait of what future bioprinting looks like.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning in 3D Bioprinting: Focus on Development of Big Data and Digital Twin

An¹,

Chua²,

Mironov³

2024

IJB

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The application of machine learning (ML) in bioprinting has attracted considerable attention recently. Many have focused on the benefits and potential of ML, but a clear overview of how ML shapes the future of three-dimensional (3D) bioprinting is still lacking. Here, it is proposed that two missing links, Big Data and Digital Twin, are the key to articulate the vision of future 3D bioprinting. Creating training databases from Big Data curation and building digital twins of human organs with cellular resolution and properties are the most important and urgent challenges. With these missing links, it is envisioned that future 3D bioprinting will become more digital and in silico, and eventually strike a balance between virtual and physical experiments toward the most efficient utilization of bioprinting resources. Furthermore, the virtual component of bioprinting and biofabrication, namely, digital bioprinting, will become a new growth point for digital industry and information technology in future.

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Deep learning for fabrication and maturation of 3D bioprinted tissues and organs

Cited by 116 publications

References 123 publications

Bioprinting of Collagen: Considerations, Potentials, and Applications

Bioprinting of Collagen: Considerations, Potentials, and Applications

Polymer-Based Scaffolds for Soft-Tissue Engineering

Application of Machine Learning in 3D Bioprinting: Focus on Development of Big Data and Digital Twin

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