Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/41633
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dc.contributor.authorKoch, Miriam-
dc.contributor.authorArlandini, Claudio-
dc.contributor.authorAntonopoulos, Gregory-
dc.contributor.authorBaretta, Alessia-
dc.contributor.authorBeaujean, Pierre-
dc.contributor.authorBEX, Geert Jan-
dc.contributor.authorBiancolini, Marco Evangelos-
dc.contributor.authorCeli, Simona-
dc.contributor.authorCosta, Emiliano-
dc.contributor.authorDrescher, Lukas-
dc.contributor.authorEleftheriadis, Vasileios-
dc.contributor.authorFadel, Nur A.-
dc.contributor.authorFink, Andreas-
dc.contributor.authorGalbiati, Federica-
dc.contributor.authorHatzakis, Ilias-
dc.contributor.authorHompis, Georgios-
dc.contributor.authorLewandowski, Natalie-
dc.contributor.authorMemmolo, Antonio-
dc.contributor.authorMensch, Carl-
dc.contributor.authorObrist, Dominik-
dc.contributor.authorPaneta, Valentina-
dc.contributor.authorPapadimitroulas, Panagiotis-
dc.contributor.authorPetropoulos, Konstantinos-
dc.contributor.authorPorziani, Stefano-
dc.contributor.authorSavvidis, Georgios-
dc.contributor.authorSethia, Khyati-
dc.contributor.authorStrakos, Petr-
dc.contributor.authorSvobodova, Petra-
dc.contributor.authorVignali, Emanuele-
dc.contributor.editorLewandowski, Natalie-
dc.contributor.editorKoller, Bastian-
dc.date.accessioned2023-10-27T08:48:37Z-
dc.date.available2023-10-27T08:48:37Z-
dc.date.issued2023-
dc.date.submitted2023-10-26T18:14:11Z-
dc.identifier.citationTECHNOLOGY AND HEALTH CARE, 31 (4) , p. 1509 -1523-
dc.identifier.urihttp://hdl.handle.net/1942/41633-
dc.description.abstractBACKGROUND: To say data is revolutionising the medical sector would be a vast understatement. The amount of medical data available today is unprecedented and has the potential to enable to date unseen forms of healthcare. To process this huge amount of data, an equally huge amount of computing power is required, which cannot be provided by regular desktop computers. These areas can be (and already are) supported by High-Performance-Computing (HPC), High-Performance Data Analytics (HPDA), and AI (together "HPC +"). OBJECTIVE: This overview article aims to show state-of-the-art examples of studies supported by the National Competence Centres (NCCs) in HPC+ within the EuroCC project, employing HPC, HPDA and AI for medical applications. METHOD: The included studies on different applications of HPC in the medical sector were sourced from the National Competence Centres in HPC and compiled into an overview article. Methods include the application of HPC+ for medical image processing, high-performance medical and pharmaceutical data analytics, an application for pediatric dosimetry, and a cloud-based HPC platform to support systemic pulmonary shunting procedures. RESULTS: This article showcases state-of-the-art applications and large-scale data analytics in the medical sector employing HPC+ within surgery, medical image processing in diagnostics, nutritional support of patients in hospitals, treating congenital heart diseases in children, and within basic research. CONCLUSION: HPC + support scientific fields from research to industrial applications in the medical area, enabling researchers to run faster and more complex calculations, simulations and data analyses for the direct benefit of patients, doctors, clinicians and as an accelerator for medical research.-
dc.description.sponsorshipThe project EuroCC (including all NCCs’ work presented here) has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement no 951732. The JU received support from the European Union’s Horizon 2020 Research and Innovation Programme and Germany, Bulgaria, Austria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, Greece, Hungary, Ireland, Italy, Lithuania, Latvia, Poland, Portugal, Romania, Slovenia, Spain, Sweden, United Kingdom, France, The Netherlands, Belgium, Luxembourg, Slovakia, Norway, Switzerland, Turkey, Republic of North Macedonia, Iceland, and Montenegro. Section 2: This work was also financially supported by the PRACE project funded in part by the EU’s Horizon 2020 Research and Innovation Programme (2014–2020) under grant agreement no. 823767. Section 3: The work described for the NCC Switzerland has also been supported by the Platform for Advanced Scientific Computing PASC. Section 4: The experiment “PediDose” received funding from the European High-Performance Computing Joint Undertaking (JU) through the FF4EuroHPC project under grant agreement no 951745. The JU received support from the European Union’s Horizon 2020 Research and Innovation Programme and Germany, Italy, Slovenia, France, and Spain. Section 5: This project received funding from the experiment number 1006 of the FF4EuroHPC project no. 951745 and the Marie Skłodowska-Curie grant agreement MeDiTATe project no 859836.-
dc.language.isoen-
dc.publisherIOS PRESS-
dc.rights2023 – The authors. Published by IOS Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (CC BY-NC 4.0).-
dc.subject.otherComputer simulation-
dc.subject.othercomputational modeling-
dc.subject.otherdata analysis-
dc.subject.otherAI (artificial intelligence)-
dc.subject.othermedicine-
dc.subject.othertherapeutics-
dc.subject.otherdiagnosis-
dc.titleHPC plus in the medical field: Overview and current examples-
dc.typeJournal Contribution-
dc.identifier.epage1523-
dc.identifier.issue4-
dc.identifier.spage1509-
dc.identifier.volume31-
local.format.pages15-
local.bibliographicCitation.jcatA1-
dc.description.notesKoch, M (corresponding author), High Performance Comp Ctr Stuttgart HLRS, Nobelstr 19, D-70569 Stuttgart, Germany.-
dc.description.noteskoch@hlrs.de-
local.publisher.placeNIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS-
local.type.refereedRefereed-
local.type.specifiedArticle-
local.type.programmeH2020-
local.relation.h2020823767-
dc.identifier.doi10.3233/THC-229015-
dc.identifier.pmid36641699-
dc.identifier.isi001029075200032-
dc.contributor.orcidCosta, Emiliano/0000-0002-0598-0728-
local.provider.typewosris-
local.description.affiliation[Koch, Miriam; Lewandowski, Natalie] High Performance Comp Ctr Stuttgart HLRS, Stuttgart, Germany.-
local.description.affiliation[Arlandini, Claudio; Memmolo, Antonio] CINECA, Casalecchio Di Reno, Italy.-
local.description.affiliation[Antonopoulos, Gregory; Petropoulos, Konstantinos] iKnowHow, Athens, Greece.-
local.description.affiliation[Baretta, Alessia] InSilicoTrials, Trieste, Italy.-
local.description.affiliation[Beaujean, Pierre] Univ Namur, Namur Inst Structured Matter, Lab Theoret Chem, Namur, Belgium.-
local.description.affiliation[Bex, Geert Jan] Hasselt Univ, Data Sci Inst, Hasselt, Belgium.-
local.description.affiliation[Biancolini, Marco Evangelos] RBF Morph, Rome, Italy.-
local.description.affiliation[Celi, Simona; Vignali, Emanuele] BioCardioLab, Fdn Toscana G Monasterio, Massa, Italy.-
local.description.affiliation[Costa, Emiliano; Galbiati, Federica] RINA, Rome, Italy.-
local.description.affiliation[Fink, Andreas] Swiss Natl Supercomp Ctr CSCS, Lugano, Switzerland.-
local.description.affiliation[Paneta, Valentina; Papadimitroulas, Panagiotis; Savvidis, Georgios] BIOEMTECH, Athens, Greece.-
local.description.affiliation[Hatzakis, Ilias] GRNET, Athens, Greece.-
local.description.affiliation[Mensch, Carl] Univ Antwerp, Dept Math, Fac Sci, Antwerp, Belgium.-
local.description.affiliation[Obrist, Dominik] Univ Bern, Bern, Switzerland.-
local.description.affiliation[Sethia, Khyati; Strakos, Petr; Svobodova, Petra] VSB Tech Univ Ostrava, IT4Innovat, Ostrava, Czech Republic.-
local.uhasselt.internationalyes-
item.validationecoom 2024-
item.contributorKoch, Miriam-
item.contributorArlandini, Claudio-
item.contributorAntonopoulos, Gregory-
item.contributorBaretta, Alessia-
item.contributorBeaujean, Pierre-
item.contributorBEX, Geert Jan-
item.contributorBiancolini, Marco Evangelos-
item.contributorCeli, Simona-
item.contributorCosta, Emiliano-
item.contributorDrescher, Lukas-
item.contributorEleftheriadis, Vasileios-
item.contributorFadel, Nur A.-
item.contributorFink, Andreas-
item.contributorGalbiati, Federica-
item.contributorHatzakis, Ilias-
item.contributorHompis, Georgios-
item.contributorLewandowski, Natalie-
item.contributorMemmolo, Antonio-
item.contributorMensch, Carl-
item.contributorObrist, Dominik-
item.contributorPaneta, Valentina-
item.contributorPapadimitroulas, Panagiotis-
item.contributorPetropoulos, Konstantinos-
item.contributorPorziani, Stefano-
item.contributorSavvidis, Georgios-
item.contributorSethia, Khyati-
item.contributorStrakos, Petr-
item.contributorSvobodova, Petra-
item.contributorVignali, Emanuele-
item.contributorKoller, Bastian-
item.fullcitationKoch, Miriam; Arlandini, Claudio; Antonopoulos, Gregory; Baretta, Alessia; Beaujean, Pierre; BEX, Geert Jan; Biancolini, Marco Evangelos; Celi, Simona; Costa, Emiliano; Drescher, Lukas; Eleftheriadis, Vasileios; Fadel, Nur A.; Fink, Andreas; Galbiati, Federica; Hatzakis, Ilias; Hompis, Georgios; Lewandowski, Natalie; Memmolo, Antonio; Mensch, Carl; Obrist, Dominik; Paneta, Valentina; Papadimitroulas, Panagiotis; Petropoulos, Konstantinos; Porziani, Stefano; Savvidis, Georgios; Sethia, Khyati; Strakos, Petr; Svobodova, Petra & Vignali, Emanuele (2023) HPC plus in the medical field: Overview and current examples. In: TECHNOLOGY AND HEALTH CARE, 31 (4) , p. 1509 -1523.-
item.fulltextWith Fulltext-
item.accessRightsOpen Access-
crisitem.journal.issn0928-7329-
crisitem.journal.eissn1878-7401-
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