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      HPC+ in the medical field: Overview and current examples

      research-article
      a , * , b , c , d , e , f , g , h , i , j , k , j , j , i , l , c , a , b , m , n , k , k , c , g , k , o , o , o , h
      ,
      Technology and Health Care
      IOS Press
      Computer simulation, computational modeling, data analysis, AI (artificial intelligence), medicine, therapeutics, diagnosis

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

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          Most cited references22

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          3D Slicer as an image computing platform for the Quantitative Imaging Network.

          Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open-source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer. Copyright © 2012 Elsevier Inc. All rights reserved.
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            The immersed boundary method

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                Author and article information

                Journal
                Technol Health Care
                Technol Health Care
                THC
                Technology and Health Care
                IOS Press (Nieuwe Hemweg 6B, 1013 BG Amsterdam, The Netherlands )
                0928-7329
                1878-7401
                5 January 2023
                30 June 2023
                2023
                : 31
                : 4 , Transforming Medical Sciences with High-Performance Computing, High-Performance Data Analytics and AI
                : 1509-1523
                Affiliations
                [a ]High-Performance Computing Center Stuttgart (HLRS), Stuttgart, Germany
                [b ]CINECA, Casalecchio di Reno, Italy
                [c ]iKnowHow, Athens, Greece
                [d ]InSilicoTrials, Trieste, Italy
                [e ]Laboratory of Theoretical Chemistry, Namur Institute of Structured Matter, University of Namur , Namur, Belgium
                [f ]Data Science Institute, Hasselt University , Hasselt, Belgium
                [g ]RBF Morph, Rome, Italy
                [h ]BioCardioLab, Fondazione Toscana G Monasterio, Massa, Italy
                [i ]RINA, Rome, Italy
                [j ]Swiss National Supercomputing Centre (CSCS), Lugano, Switzerland
                [k ]BIOEMTECH, Athens, Greece
                [l ]GRNET, Athens, Greece
                [m ]Department of Mathematics, Faculty of Science, University of Antwerp , Antwerp, Belgium
                [n ]University of Bern , Bern, Switzerland
                [o ]IT4Innovations, VSB – Technical University of Ostrava , Ostrava-Poruba, Czech Republic
                Author notes
                [* ]Corresponding author: Miriam Koch, High-Performance Computing Center Stuttgart (HLRS), Nobelstr. 19, 70569 Stuttgart, Germany. E-mail: koch@ 123456hlrs.de .
                Article
                THC229015
                10.3233/THC-229015
                10357138
                36641699
                c369f7a6-f490-487e-a8ed-1ff4cf9679bf
                © 2023 – The authors. Published by IOS Press.

                This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 7 November 2022
                : 9 December 2022
                Categories
                Research Article

                computer simulation,computational modeling,data analysis,ai (artificial intelligence),medicine,therapeutics,diagnosis

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