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Poster: Inverse Design Process: New Methodology to Design Medical Devices with BIG DATA

Research Publication

Poster: Inverse Design Process: New Methodology to Design Medical Devices with BIG DATA. Bethany Tourek, Daniel Orban, Bogden Tanasoiu, Hakizumwami Birali Runesha, Daniel F. Keefe, Arthur G. Erdman. Minnesota Supercomputing Institute Research Exhibition (2016)

Notes

Grand Prize Winner (Physical Sciences and Engineering)

Abstract

Each year millions of dollars are spent on devices to improve individual patient health. These devices came to fruition through a linear design process. To reach a final design many ideas are eliminated without complete consideration of the potential impact. This linear process is limited and significantly bounds the design space by discouraging complete exploration of all ideas. This work focuses on reversing the traditional forward design process, to create a more efficient and effective pipeline for medical device design. The process capitalizes on computational speeds, simulation models and visualization environments to prepare specific output parameters for each design to be visually displayed and presented to the user for inspection and refinement until an optimized design is selected. The inverse design process utilizes BIG DATA to fill the design space with device models that vary in dimensions and environment conditions to create design instances and find potential solutions. A cardiac lead is an ideal device to prove the effectiveness of BIG DATA within the inverse design process. Understanding and modeling the complex environment of the heart would improve the design of the cardiac leads and inform surgeons of patient specific complexities that impact selection of cardiac lead devices. The inverse design process starts with a computer-aided design (CAD) model of a device. The devices are structurally analyzed through finite element analysis (FEA), blood flow is modeled through computational fluid dynamics (CFD), and tissue fibrosis is modeled to understand the environment surrounding the cardiac lead model. Each design space will include hundreds of design instances, while each design instance will include an anatomical model, structural analysis, fluid flow analysis and fibrosis modeling.

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