Bioink Formulation and Machine Learning-Empowered Bioprinting Optimization.

additive biomanufacturing biofabrication bioink bioink formation biomaterials bioprinting machine learning tissue engineering

Journal

Frontiers in bioengineering and biotechnology
ISSN: 2296-4185
Titre abrégé: Front Bioeng Biotechnol
Pays: Switzerland
ID NLM: 101632513

Informations de publication

Date de publication:
2022
Historique:
received: 05 04 2022
accepted: 18 05 2022
entrez: 5 7 2022
pubmed: 6 7 2022
medline: 6 7 2022
Statut: epublish

Résumé

Bioprinting enables the fabrication of complex, heterogeneous tissues through robotically-controlled placement of cells and biomaterials. It has been rapidly developing into a powerful and versatile tool for tissue engineering. Recent advances in bioprinting modalities and biofabrication strategies as well as new materials and chemistries have led to improved mimicry and development of physiologically relevant tissue architectures constituted with multiple cell types and heterogeneous spatial material properties. Machine learning (ML) has been applied to accelerate these processes. It is a new paradigm for bioprinting. In this review, we explore current trends in bioink formulation and how ML has been used to accelerate optimization and enable real-time error detection as well as to reduce the iterative steps necessary for bioink formulation. We examined how rheometric properties, including shear storage, loss moduli, viscosity, shear-thinning property of biomaterials affect the printability of a bioink. Furthermore, we scrutinized the interplays between yield shear stress and the printability of a bioink. Moreover, we systematically surveyed the application of ML in precision in situ surgical site bioprinting, closed-loop AI printing, and post-printing optimization.

Identifiants

pubmed: 35782492
doi: 10.3389/fbioe.2022.913579
pii: 913579
pmc: PMC9240914
doi:

Types de publication

Journal Article Review

Langues

eng

Pagination

913579

Informations de copyright

Copyright © 2022 Freeman, Calabro, Williams, Jin and Ye.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Sebastian Freeman (S)

Department of Biomedical Engineering, Binghamton University, Binghamton, NY, United States.

Stefano Calabro (S)

Department of Biomedical Engineering, Binghamton University, Binghamton, NY, United States.

Roma Williams (R)

Department of Biomedical Engineering, Binghamton University, Binghamton, NY, United States.
Department of Biomedical Engineering, University of Miami, Coral Gables, FL, United States.

Sha Jin (S)

Department of Biomedical Engineering, Binghamton University, Binghamton, NY, United States.
Center of Biomanufacturing for Regenerative Medicine, Binghamton University, State University of New York (SUNY), Binghamton, NY, United States.

Kaiming Ye (K)

Department of Biomedical Engineering, Binghamton University, Binghamton, NY, United States.
Center of Biomanufacturing for Regenerative Medicine, Binghamton University, State University of New York (SUNY), Binghamton, NY, United States.

Classifications MeSH