First step is to understand the relevant mechanisms and physics. After this we will work on a case to demonstrate the simulation methodology we follow, potentially in a basic case. Once this is established, we can either work together with you to evaluate the device, or we can do the work and present a standard report. In-silico evaluation can vary in complexity: While in early design phases, ‘simple’ isolated device evaluations are the most efficient ones; just before or during the animal trials, full-fledged simulations considering the deployment are the most suitable ones.

If experimental data exists, this can further help with the validation of the methodology and tools used, so that for the variant simulations (design or deployment revisions) the confidence interval is kept high.

No. However, we have a wide partner network through which we perform simulation driven medical device testing and evaluation. This increases the efficiency of in-vitro/in-vivo trials, and reduces the number of experimental runs significantly.

Medividia isn’t a software vendor. However, once we come to a mature phase in a simulation work, we can work on technology and knowledge transfer. At this phase, we can establish necessary contacts with our software vendor partners so that you are supported during this phase.

Pilot projects are risk-shared projects. If we foresee that establishing a simulation concept in a particular case is difficult or we do not have prior experimental validation on similar cases, we will treat it as a pilot work. The cost of a pilot work is a function of the physics and simulation challenges involved and directly associated with the man-hour to be spent.

Our master standard in simulating medical devices is ASME V&V 40 (Assessing Credibility of Computational Modeling through Verification and Validation: Application to Medical Devices), ASME V&V 20 and ASME V&V 10. Others such as ASTM F2079, ASTM F3067, ISO 25539-1 & 2, ISO 5840-1 & 2 & 3; will also be considered when relevant.

Thanks to the recent breakthroughs in medical imaging and AI-driven post processing technologies, it is now possible to get accurate enough geometry definitions of structures of interest in most of the cases. However, patient specific simulations cannot be performed only by using patient geometry data. In many scenarios, patient specific blood flow data is also required. By using internal or partner libraries coupled with flow simulation software, we are able to incorporate both patient specific geometries and flow data in our simulations.

Depends. We work with various hospital laboratories to make use of simulation data in various cases. These could be grouped as follows: Understanding the effects of certain pathologies, patient specific device selection, statistically created patient model specific device selection, device-host interaction.