HPC+ in the medical field: Overview and current examples

Author:

Koch Miriam1,Arlandini Claudio2,Antonopoulos Gregory3,Baretta Alessia4,Beaujean Pierre5,Bex Geert Jan6,Biancolini Marco Evangelos7,Celi Simona8,Costa Emiliano9,Drescher Lukas10,Eleftheriadis Vasileios11,Fadel Nur A.10,Fink Andreas10,Galbiati Federica9,Hatzakis Ilias12,Hompis Georgios3,Lewandowski Natalie1,Memmolo Antonio2,Mensch Carl13,Obrist Dominik14,Paneta Valentina11,Papadimitroulas Panagiotis11,Petropoulos Konstantinos3,Porziani Stefano7,Savvidis Georgios11,Sethia Khyati15,Strakos Petr15,Svobodova Petra15,Vignali Emanuele8

Affiliation:

1. High-Performance Computing Center Stuttgart (HLRS), Stuttgart, Germany

2. CINECA, Casalecchio di Reno, Italy

3. iKnowHow, Athens, Greece

4. InSilicoTrials, Trieste, Italy

5. Laboratory of Theoretical Chemistry, Namur Institute of Structured Matter, University of Namur, Namur, Belgium

6. Data Science Institute, Hasselt University, Hasselt, Belgium

7. RBF Morph, Rome, Italy

8. BioCardioLab, Fondazione Toscana G Monasterio, Massa, Italy

9. RINA, Rome, Italy

10. Swiss National Supercomputing Centre (CSCS), Lugano, Switzerland

11. BIOEMTECH, Athens, Greece

12. GRNET, Athens, Greece

13. Department of Mathematics, Faculty of Science, University of Antwerp, Antwerp, Belgium

14. University of Bern, Bern, Switzerland

15. IT4Innovations, VSB – Technical University of Ostrava, Ostrava-Poruba, Czech Republic

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.

Publisher

IOS Press

Subject

Health Informatics,Biomedical Engineering,Information Systems,Biomaterials,Bioengineering,Biophysics

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