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

Jia An, Chee Kai Chua, Vladimir Mironov

Article ID: 342
Vol 7, Issue 1, 2021, Article identifier:342

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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.


3D bioprinting; Complexity; Machine learning; Big data; Digital twin

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