What makes computational modeling and simulation of medical devices so unique? It is the same thing that makes them medical— the interaction with biology. When you are running a stress analysis on a device mechanism or a thermal analysis on enclosed electronics, the modeling is the same for medical devices as for other products. When your questions involve the interaction with tissue the simulations start to take on elements that are unique to medical devices.
Consider the following analyses:
- What laser wavelength and power will provide the desired surgical result for our new device concept?
- How much more power do we have to supply at the electrosurgery generator if we increase the size of our deployable electrode by 20%?
- What are the performance expectations in human trials for our device that has been developed using porcine models?
- Is our sensor placement optimized for the physiological parameter we want to measure?
- If we change our design/manufacturing method/algorithm to improve the device manufacturability, will it change the surgical effect in unintended ways?
Every one of these scenarios requires a computational model for biological materials in order to analyze with simulation tools. These tissue models range from simple to complex: from static and isotropic defined materials to nonlinear and partially reversible multiscale linked physical mechanisms accounting for anisotropy in tissue layers and induced biological response (like heat-shock or healing). Understanding the material science of biological tissue becomes key as the need for accurate and timely simulations comes to the forefront of device design.
Another complicating factor for device design is the variability of material parameters seen not only between but also within patients. Things like age, hydration, disease state, or scar tissue all play a role in tissue variability and quickly outstrip the relevance of static material properties. The ability of computational modeling and simulation to provide insight into the sensitivity of the result to the variability of the multitude of system inputs provides a powerful tool to know if the new design is likely to be effective across a wide range of patient populations. As simulation data is included more and more in 510(k) and PMA submissions to the FDA, knowing the key sensitive inputs to the device system will be the new expectation.
Arlen Ward, PhD, PE is a Modeling and Simulation Principal for System Insight Engineering, LLC. SIE provides computational modeling and simulation services for medical device companies with a focus on the interaction between energy and tissue. Follow SIE on Twitter or visit the company website at systeminsightengineering.com for more information.