Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/74753

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dc.contributor.authorCosta, João Pedro Bebianopor
dc.contributor.authorSilva-Correia, Joanapor
dc.contributor.authorReis, R. L.por
dc.contributor.authorOliveira, J. M.por
dc.date.accessioned2021-11-22T12:19:28Z-
dc.date.issued2021-02-
dc.date.submitted2021-11-
dc.identifier.citationCosta 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, 2021por
dc.identifier.issn2059-4755por
dc.identifier.urihttps://hdl.handle.net/1822/74753-
dc.description.abstractBioengineering 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.por
dc.description.sponsorshipThe authors acknowledge financial support provided through projects B-FABULUS (PTDC/BBB-ECT/2690/2014), Fun4TE (PTDC/EMD-EMD/31367/2017) and JUSThera (ref: NORTE-01-0145-FEDER-000055), financed by the Portuguese Foundation for Science and Technology (FCT) and co-financed by European Regional Development Fund (FEDER) and Operational Program for Competitiveness and Internationalization (POCI). JB Costa acknowledges the Junior Researcher contract (POCI-01-0145-FEDER 031367) attributed by FCT to Fun4TE. The FCT distinction attributed to J Silva-Correia (IF/00115/2015) under the Investigator FCT program is also greatly acknowledged.por
dc.language.isoengpor
dc.publisherFuture Medicinepor
dc.relationinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FBBB-ECT%2F2690%2F2014/PTpor
dc.relationinfo:eu-repo/grantAgreement/FCT/9471 - RIDTI/PTDC%2FEMD-EMD%2F31367%2F2017/PTpor
dc.relationinfo:eu-repo/grantAgreement/FCT/Investigador FCT/IF%2F00115%2F2015%2FCP1294%2FCT0005/PTpor
dc.rightsrestrictedAccesspor
dc.subject3D bioprintingpor
dc.subjectArtificial intelligencepor
dc.subjectBioengineeringpor
dc.subjectBiofabricationpor
dc.subjectDeep learningpor
dc.titleDeep learning in bioengineering and biofabrication: a powerful technology boosting translation from research to clinicspor
dc.typearticle-
dc.peerreviewedyespor
dc.relation.publisherversionhttps://www.futuremedicine.com/doi/10.2217/3dp-2021-0007por
dc.commentshttp://3bs.uminho.pt/node/20491por
oaire.citationStartPage191por
oaire.citationEndPage211por
oaire.citationIssue4por
oaire.citationVolume5por
dc.date.updated2021-11-22T09:36:40Z-
dc.identifier.eissn2059-4763por
dc.identifier.doi10.2217/3dp-2021-0007por
dc.date.embargo10000-01-01-
sdum.journalJournal of 3D Printing in Medicinepor
Aparece nas coleções:3B’s - Artigos em revistas/Papers in scientific journals

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