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Webinar: Coupling Data-Intensive Modeling, Simulation, and Visualization with Human Facilities for Design: Applications to Next-Generation Medical Device Prototyping

Research Publication

Webinar: Coupling Data-Intensive Modeling, Simulation, and Visualization with Human Facilities for Design: Applications to Next-Generation Medical Device Prototyping. Daniel F. Keefe, Arthur G. Erdman, Hakizumwami Birali Runesha. National Institute of Health: Interagency Modeling and Analysis Group (2016)

Abstract

In this talk, we will describe the grand opportunity we believe now exists to couple what data-intensive computing does best with what humans do best. Our long-term objective is to shift and improve the future of simulation-based engineering with big data, and our current projects investigate this possibility through specific applications to virtual prototyping of medical devices. Our approach is to couple the valuable and intense amounts of medical imaging, physical simulation, and other life sciences data that are generated today with new computational tools that not only support automated data analysis but also powerfully leverage our own human capabilities to see, touch, explore, and analyze. By adopting a human-centric approach to big data science, including significant new research in the areas of data visualization and human-computer interfaces, we aim to not only accelerate basic research and discovery but also make the results of big data science accessible to doctors, medical device engineers, and countless other creative thinkers who do not necessarily have a core background in computational methods. Our current work includes applications to two types of medical devices: cardiac leads and mechanical biopsy devices. We will present recent results from the three key interdisciplinary perspectives on our team: (1) adapting and extending high-performance computing techniques to model and simulate medical devices, (2) developing new interactive, visual design environments for working more effectively with the massive datasets produced via simuliation, and (3) applying new data-intensive workflows to real medical device modeling and simulation problems in both academia and industry.

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