HPC+ in the medical field: Overview and current examples.

Miriam Koch, Claudio Arlandini, Gregory Antonopoulos, Alessia Baretta, Pierre Beaujean, Geert Jan Bex, Marco Evangelos Biancolini, Simona Celi, Emiliano Costa, Lukas Drescher, Vasileios Eleftheriadis, Nur A Fadel, Andreas Fink, Federica Galbiati, Ilias Hatzakis, Georgios Hompis, Natalie Lewandowski, Antonio Memmolo, Carl Mensch, Dominik Obrist, Valentina Paneta, Panagiotis Papadimitroulas, Konstantinos Petropoulos, Stefano Porziani, Georgios Savvidis, Khyati Sethia, Petr Strakos, Petra Svobodova, Emanuele Vignali
Author Information
  1. Miriam Koch: High-Performance Computing Center Stuttgart (HLRS), Stuttgart, Germany.
  2. Claudio Arlandini: CINECA, Casalecchio di Reno, Italy.
  3. Gregory Antonopoulos: iKnowHow, Athens, Greece.
  4. Alessia Baretta: InSilicoTrials, Trieste, Italy.
  5. Pierre Beaujean: Laboratory of Theoretical Chemistry, Namur Institute of Structured Matter, University of Namur, Namur, Belgium.
  6. Geert Jan Bex: Data Science Institute, Hasselt University, Hasselt, Belgium.
  7. Marco Evangelos Biancolini: RBF Morph, Rome, Italy.
  8. Simona Celi: BioCardioLab, Fondazione Toscana G Monasterio, Massa, Italy.
  9. Emiliano Costa: RINA, Rome, Italy.
  10. Lukas Drescher: Swiss National Supercomputing Centre (CSCS), Lugano, Switzerland.
  11. Vasileios Eleftheriadis: BIOEMTECH, Athens, Greece.
  12. Nur A Fadel: Swiss National Supercomputing Centre (CSCS), Lugano, Switzerland.
  13. Andreas Fink: Swiss National Supercomputing Centre (CSCS), Lugano, Switzerland.
  14. Federica Galbiati: RINA, Rome, Italy.
  15. Ilias Hatzakis: GRNET, Athens, Greece.
  16. Georgios Hompis: iKnowHow, Athens, Greece.
  17. Natalie Lewandowski: High-Performance Computing Center Stuttgart (HLRS), Stuttgart, Germany.
  18. Antonio Memmolo: CINECA, Casalecchio di Reno, Italy.
  19. Carl Mensch: Department of Mathematics, Faculty of Science, University of Antwerp, Antwerp, Belgium.
  20. Dominik Obrist: University of Bern, Bern, Switzerland.
  21. Valentina Paneta: BIOEMTECH, Athens, Greece.
  22. Panagiotis Papadimitroulas: BIOEMTECH, Athens, Greece.
  23. Konstantinos Petropoulos: iKnowHow, Athens, Greece.
  24. Stefano Porziani: RBF Morph, Rome, Italy.
  25. Georgios Savvidis: BIOEMTECH, Athens, Greece.
  26. Khyati Sethia: IT4Innovations, VSB - Technical University of Ostrava, Ostrava-Poruba, Czech Republic.
  27. Petr Strakos: IT4Innovations, VSB - Technical University of Ostrava, Ostrava-Poruba, Czech Republic.
  28. Petra Svobodova: IT4Innovations, VSB - Technical University of Ostrava, Ostrava-Poruba, Czech Republic.
  29. Emanuele Vignali: BioCardioLab, Fondazione Toscana G Monasterio, Massa, Italy.

Abstract

BACKGROUND: 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.

Keywords

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MeSH Term

Child
Humans
Computing Methodologies
Image Processing, Computer-Assisted
Software

Word Cloud

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