期刊
INTERNATIONAL JOURNAL OF BIOPRINTING
卷 7, 期 1, 页码 1-6出版社
WHIOCE PUBL PTE LTD
DOI: 10.18063/ijb.v7i1.342
关键词
3D bioprinting; Complexity; Machine learning; Big data; Digital twin
The application of machine learning in bioprinting has attracted attention, and addressing Big Data and Digital Twin is crucial for the future development of 3D bioprinting.
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 curadon 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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