Machine Learning-Guided Three-Dimensional Printing of Tissue Engineering Scaffolds

Conev, Anja; Litsa, Eleni; Perez, Marissa R; Diba, Mani; Mikos, Antonios G.; Kavraki, Lydia E.

doi:10.1089/ten.tea.2020.0191

Cited by 70 publications

(52 citation statements)

References 29 publications

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“…The random forest regression, random forest classification, and linear regression models created can be used to an extent in conjunction with one another for outcome prediction as well as condition recommendation. Specifically, the random forest classification models for cell viability and filament diameter predictions were able to generate similar average model accuracy scores compared to previous literature's model performance accuracy also using random forest models [12]. Both the random forest and linear regression models, more so the linear regression models, have shown the ability to represent several physical phenomena that have been documented in previous bioprinting or hydrogel studies.…”

Section: Discussionsupporting

confidence: 59%

“…Compared to other ML models created for bioprinting predictions, the regression models created in this study provided lower R 2 values and comparable errors with a similar proportion of training data to test data, and the accuracy of the classification models were lower as well [12,15]. A major reason for this is the difference in experiment variation for the datasets used to create the models.…”

Section: Discussionmentioning

confidence: 83%

“…In addition, our dataset is inherently heterogeneous due to being acquired from studies conducted using different testing conditions and printing strategies. Comparatively, past studies contained larger amounts of data collected from controlled experimental settings [12]. Not all of the input conditions used in developing the ML models were reported in every study included in the dataset.…”

Section: Discussionmentioning

confidence: 99%

“…This process resulted in needing 4 to 47 experiments to find optimal parameter combinations compared to using a total possible number of experiments ranging from 6000 to 10,000 determined by the Bayesian Optimization algorithm. Conev et al also examined random forest regressor and classifier capabilities in determining printed construct quality using a previous EBB dataset containing systematic examination of poly(propylene fumarate) [12,13]. Results indicated satisfactory labeling performance from both random forest models.…”

Section: Introductionmentioning

confidence: 99%

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Machine Assisted Experimentation of Extrusion-Based Bioprinting Systems

et al. 2021

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Optimization of extrusion-based bioprinting (EBB) parameters have been systematically conducted through experimentation. However, the process is time- and resource-intensive and not easily translatable to other laboratories. This study approaches EBB parameter optimization through machine learning (ML) models trained using data collected from the published literature. We investigated regression-based and classification-based ML models and their abilities to predict printing outcomes of cell viability and filament diameter for cell-containing alginate and gelatin composite bioinks. In addition, we interrogated if regression-based models can predict suitable extrusion pressure given the desired cell viability when keeping other experimental parameters constant. We also compared models trained across data from general literature to models trained across data from one literature source that utilized alginate and gelatin bioinks. The results indicate that models trained on large amounts of data can impart physical trends on cell viability, filament diameter, and extrusion pressure seen in past literature. Regression models trained on the larger dataset also predict cell viability closer to experimental values for material concentration combinations not seen in training data of the single-paper-based regression models. While the best performing classification models for cell viability can achieve an average prediction accuracy of 70%, the cell viability predictions remained constant despite altering input parameter combinations. Our trained models on bioprinting literature data show the potential usage of applying ML models to bioprinting experimental design.

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Section: Discussionsupporting

confidence: 59%

Section: Discussionmentioning

confidence: 83%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Assisted Experimentation of Extrusion-Based Bioprinting Systems

et al. 2021

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“…The end of the first decade of this century has brought a significant change in the approach to the problem of tissue scaffold design [91]. At that time, the first attempts were made to use optimization algorithms, both classical ones and those based on artificial intelligence methods [92][93][94].…”

Section: Computer-assisted Optimization Of Te Scaffoldsmentioning

confidence: 99%

Advances in 3D Printing for Tissue Engineering

et al. 2021

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Tissue engineering (TE) scaffolds have enormous significance for the possibility of regeneration of complex tissue structures or even whole organs. Three-dimensional (3D) printing techniques allow fabricating TE scaffolds, having an extremely complex structure, in a repeatable and precise manner. Moreover, they enable the easy application of computer-assisted methods to TE scaffold design. The latest additive manufacturing techniques open up opportunities not otherwise available. This study aimed to summarize the state-of-art field of 3D printing techniques in applications for tissue engineering with a focus on the latest advancements. The following topics are discussed: systematics of the available 3D printing techniques applied for TE scaffold fabrication; overview of 3D printable biomaterials and advancements in 3D-printing-assisted tissue engineering.

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Application of Machine Learning Algorithms and Models in 3D Printing

Singh¹

2022

Cognitive Computing Models in Communication Systems

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Machine Learning-Guided Three-Dimensional Printing of Tissue Engineering Scaffolds

Cited by 70 publications

References 29 publications

Machine Assisted Experimentation of Extrusion-Based Bioprinting Systems

Machine Assisted Experimentation of Extrusion-Based Bioprinting Systems

Advances in 3D Printing for Tissue Engineering

Application of Machine Learning Algorithms and Models in 3D Printing

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