2019 BMES/FDA Frontiers in Medical Devices Conference

SmartUQ at 2019 BMES/FDA Frontiers in Medical Devices Conference

Washington, DC
March 19 - 21

We invite you to stop by our booth #11 at 2019 BMES/FDA Frontiers in Medical Devices Conference; meet experts in engineering analytics and uncertainty quantification, see demonstrations, and explore how SmartUQ can improve your analysis.

2019 BMES/FDA Frontiers in Medical Devices Conference

Conference Presentation

Modern Uncertainty Quantification with SmartUQ

March 20 - 1:30 PM to 2:00 PM - Room 1105
Presented by Gavin Jones, Application Engineer

SmartUQ is a predictive analytics tool for reducing the time, cost, and risk of solving complex data and engineering problems. With the evergrowing abundance of complex systems and data, SmartUQ's engineering analytics and Uncertainty Quantification tools have the solutions to fundamental questions like: What data (quantity and quality) do I need?, What resulting information is actionable?, and How do I do this efficiently under uncertainty?

Application of VVUQ Using Bayesian Calibration to Assess Credibility of a Coronary Stent Model

March 21 - 1:30 PM to 1:45 PM - General Vessey Room
Presented by Zack Graves, Sr. Application Engineer

This study presents a comprehensive methodology for robust validation of computational models that contain inherent parametric uncertainty in the pre-clinical evaluation phase of the product lifecycle. Application of this methodology can speed up the turnaround time on new medical device designs and help medical device companies hit high regulatory standards, which in turn reduces risk for patients.

Poster Presentation

Uncertainty Quantification of a Microwave Ablation Simulation with Spatial and Transient Responses

March 19 5:15 PM - 7:15 PM & March 20 - 12:00 PM to 1:30 PM - Chesapeake Room
Presented by Gavin Jones, Application Engineer

This study presents a novel methodology for both transient and spatial-transient simulation of tumor ablation treatment using surrogate modeling. This surrogate model will help ensure a microwave ablation device is able to produce predictable ablation zones while keeping ablation time to a minimum. The techniques presented with this case study can be implemented to a wide variety of other medical devices to deliver similar results and improve treatment predictions on a case-by-case basis for patients.