HPC+ in the medical field: Overview and current examples

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: AI (artificial intelligence); Computer simulation; computational modeling; data analysis; diagnosis; medicine; therapeutics.

Figure 1.

The main concept of the tool for medical image processing and analysis.

Figure 2.

Functionality of PediDose backend.

Figure 3.

PediDose software implementation. Left: GUI for clinicians to import parameters. Middle: Horizontal diagram of the predicted absorbed doses per organ. Right: Exported report in pdf format.

Figure 4.

Workflow for MDT application development.

Figure 5.

Example of interface for MDT of MBTS (a), computational times (b) and relative times percentages for the different application stages (c) as a function of number of HPC nodes.