This project harnesses mature and modern techniques for uncertainty quantification (UQ) from the computational science and mathematics communities and incorporates them into the established biomedical simulation software framework SCIRun. Just as bioelectric simulation models have become increasingly accurate at representing physical processes and geometry, so also has it become increasingly important to understand the uncertainty stemming from ignorance about precise values of model parameters. To our knowledge, success of this project represents one of the first developments of folding in modern UQ techniques into widely used, freely available software pipelines for simulation of bioelectric fields in heart and brain applications. Successful incorporation of established and proven UQ tools into SCIRun will substantially increase the utility of simulation based biomedical predictions in medical practice. Although there is a need in the research community for such tools, the number and nature of users makes commercial development very unlikely. There are no commercial tools specifically for bioelectric field modeling and while some industry uses these tools, their high intellectual cost and limited market keep them well below the threshold for commercial software.
This project harnesses mature and modern techniques that can quantify statistical uncertainty in biomedical simulations by integrating these techniques into the well-established biomedical modeling software framework, SCIRun. Biomedical simulation has become increasingly accurate at representing functions of the body, both in health and disease, however such simulations rarely include any estimates of the uncertainty stemming from ignorance about precise values of model parameters. Our proposal represents one of the first to integrate modern methods capable of quantifying such uncertainty into widely used, freely available software pipelines for simulation of bioelectric fields from the heart and brain.
