Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/74753
Título: | Deep learning in bioengineering and biofabrication: a powerful technology boosting translation from research to clinics |
Autor(es): | Costa, João Pedro Bebiano Silva-Correia, Joana Reis, R. L. Oliveira, J. M. |
Palavras-chave: | 3D bioprinting Artificial intelligence Bioengineering Biofabrication Deep learning |
Data: | Fev-2021 |
Editora: | Future Medicine |
Revista: | Journal of 3D Printing in Medicine |
Citação: | Costa J. B., Silva-Correia J., Reis R. L., Oliveira J. M. Deep Learning in Bioengineering and Biofabrication: a novel approach that can possibly boost the translation of 3D Bioprinting to clinics?, Journal of 3D Printing in Medicine , doi:10.2217/3dp-2021-0007, 2021 |
Resumo(s): | Bioengineering has been revolutionizing the production of biofunctional tissues for tackling unmet clinical needs. Bioengineers have been focusing their research in biofabrication, especially 3D bioprinting, providing cutting-edge approaches and biomimetic solutions with more reliability and cost–effectiveness. However, these emerging technologies are still far from the clinical setting and deep learning, as a subset of artificial intelligence, can be widely explored to close this gap. Thus, deep-learning technology is capable to autonomously deal with massive datasets and produce valuable outputs. The application of deep learning in bioengineering and how the synergy of this technology with biofabrication can help (more efficiently) bring 3D bioprinting to clinics, are overviewed herein. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/74753 |
DOI: | 10.2217/3dp-2021-0007 |
ISSN: | 2059-4755 |
e-ISSN: | 2059-4763 |
Versão da editora: | https://www.futuremedicine.com/doi/10.2217/3dp-2021-0007 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
Aparece nas coleções: | 3B’s - Artigos em revistas/Papers in scientific journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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20491-3dp-2021-0007.pdf Acesso restrito! | 5,89 MB | Adobe PDF | Ver/Abrir |