The future of bone regeneration: integrating AI into tissue engineering

MacKay, Benita; Marshall, Karen; Grant-Jacob, James; Kanczler, Janos M.; Eason, R.W.; Oreffo, Richard O.C.; Mills, B.

doi:10.1088/2057-1976/ac154f

Cited by 36 publications

(14 citation statements)

References 136 publications

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“…the second section sets the value for the comprForce and compressionAxis The following Sections describe the performed experiments and their results, including the learning performance along the training process and the generated optimal protocols, showing the impact of different hyperparameters combinations over learning performance. Since an exhaustive DSE of the simulated biofabrication processes targets is unfeasible in finite computational times [4], results aim to analyze the evolution of the learning processes reaching optimality rather than its convergence to a global optimum.…”

Section: Optimized Protocol Generationmentioning

confidence: 99%

“…25.538212 doi: bioRxiv preprint tissue integration and homeostasis are still far from reach [3]. In these domains, the complexity of target biological systems and the intrinsic uncertainty in their behavior limits the capability to design optimal products and reliably control their structure and function during biofabrication [4], limiting products' clinical relevance. The biofabrication processes' design space is a multidimensional region where input variables and process parameters determine the final product quality [5].…”

Section: Introductionmentioning

confidence: 99%

“…The biofabrication processes' design space is a multidimensional region where input variables and process parameters determine the final product quality [5]. Hence, implementing an effective Design Space Exploration (DSE) of a biofabrication process is challenging and time-consuming [4]. Empirical DSE has high costs in terms of time and resources.…”

Section: Introductionmentioning

confidence: 99%

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A methodology combining reinforcement learning and simulation to optimize thein silicoculture of epithelial sheets

Castrignanò

Bardini

Savino

et al. 2023

Preprint

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Tissue Engineering and Regenerative Medicine aim to replicate and replace tissues for curing disease. However, full tissue integration and homeostasis are still far from reach. Biofabrication is an emerging field that identifies the processes required for generating biologically functional products with the desired structural organization and functionality and can potentially revolutionize the regenerative medicine domain, which aims to use patients' cells to restore the structure and function of damaged tissues and organs. However, biofabrication still has limitations in the quality of processes and products. Biofabrication processes are often improved empirically, but this is slow, costly, and provides partial results. Computational approaches can tap into biofabrication underused potential, supporting analysis, modeling, design, and optimization of biofabrication processes, speeding up their improvement towards a higher quality of products and subsequent higher clinical relevance. This work proposes a reinforcement learning-based computational design space exploration methodology to generate optimal protocols for the simulated fabrication of epithelial sheets. The optimization strategy relies on a deep reinforcement learning algorithm, the Advantage-Actor Critic, which relies on a neural network model for learning. In contrast, simulations rely on the PalaCell2D simulation framework. Validation demonstrates the proposed approach on two protocol generation targets: maximizing the final number of obtained cells and optimizing the spatial organization of the cell aggregate.Advantage-Actor Critic, which relies on a neural network model for learning. In contrast, simulations rely on the PalaCell2D simulation framework. Validation demonstrates the proposed approach on two protocol generation targets: maximizing the final number of obtained cells and optimizing the spatial organization of the cell aggregate.

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Section: Optimized Protocol Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A methodology combining reinforcement learning and simulation to optimize thein silicoculture of epithelial sheets

Castrignanò

Bardini

Savino

et al. 2023

Preprint

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“…Importantly, data science methodologies alone ignore the fundamental laws of physics and can propose nonphysical solutions 153 . Therefore, exciting efforts are being undertaken to integrate data science methodologies with mathematical modeling approaches to create robust predictive models that integrate the underlying physical principles while being able to explore the massive design spaces that characterize the TERM field [153][154][155][156] .…”

Section: What Can Models Contribute To the Future Of Term?mentioning

confidence: 99%

Implementing Computational Modeling in Tissue Engineering: Where Disciplines Meet

Post

Loerakker

Merks

et al. 2022

Tissue Engineering Part A

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published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User

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“…Currently, artificial intelligence (AI) has been used in various domains, including tissue engineering. Mackay et al stated in their paper that the future of tissue engineering belongs to artificial intelligence as it helps overcome the current challenges in medicine (Mackay et al 2021 ). Machine learning (ML) is a category of AI that processes functions such as the human learning ability in computers and it helps solve many challenges in biomaterials research without the need to perform time and resource-demanding experimentations.…”

Section: Introductionmentioning

confidence: 99%

Identification and ranking biomaterials for bone scaffolds using machine learning and PROMETHEE

et al. 2023

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Purpose Bones have a complex hierarchical structure that supports their diverse chemical, biological, and mechanical functions. High rates of bone susceptibility to fractures and injury have attracted extensive research interest to find alternate biomaterials for bone scaffolds. Natural bone healing is only successful if the defect is very small and when a defect exceeds 1 cm 3 then bone grafting is required. Large bone defects or injuries are very serious problems in orthopedics as they bring great harm to health and normal function of daily life routine. A scaffold should have good strength to maintain its own structure after implantation in a load bearing environment and without being stiff that shields surrounding bone from the load. Therefore, mechanical properties of bone scaffolds should match those of the host tissue and should be part of the natural environment of the body without any harm or further damage. Methods In this paper, we present two main contributions. First, we investigate the use of machine learning models in identifying biomaterials that are suitable for bone scaffolds. Second, we rank the best materials for biomedical scaffold applications using the multi-criteria decision analysis methods, the Preference Ranking Organization METhod for the Enrichment of Evaluations (PROMETHEE). Machine learning models investigated are AdaBoost, artificial neural network (ANN), Naïve Bayes (NB), Decision tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). Mechanical properties such as comprehensive strength, tensile strength, and Young’s modulus with the cortical bone are used as the standard reference for classification. Results The results show that the ANN outperforms the other machine learning models in identifying the biomaterials suitable for bone tissue engineering, while the ranking results using PROMETHEE show that Brushite and Titanium alloy are the best appropriate biomaterials for the cancellous and cortical bones, respectively. Conclusion Brushite and Titanium alloy are the best biomaterials for the cancellous and cortical bones, respectively.

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The future of bone regeneration: integrating AI into tissue engineering

Cited by 36 publications

References 136 publications

A methodology combining reinforcement learning and simulation to optimize thein silicoculture of epithelial sheets

A methodology combining reinforcement learning and simulation to optimize thein silicoculture of epithelial sheets

Implementing Computational Modeling in Tissue Engineering: Where Disciplines Meet

Identification and ranking biomaterials for bone scaffolds using machine learning and PROMETHEE

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