Optimizing Complex Cardiac Device Development

One Fortune 500 medical device company aims to carve out a niche by addressing complicated cases such as bicuspid aortic valves, failed TAVR or other surgical valves, and aortic regurgitation. Together, these smaller specialties represent a significant number of potential patients globally. A cardiac clinical engineer from the company is passionate about bridging the gap between research and development (R&D) and clinical applications, focusing on challenging cases in structural heart therapy.

To tackle these cases, this company leverages the Materialise Mimics software for advanced 3D modeling and printing. The workflow involves segmenting CT scan data using the Mimics AI Heart Segmentation algorithm to create detailed 3D models — virtual and in special cases, physical 3D prints. The anatomic segmentation shows the existing valve structures, the current placement of devices, and potential positioning for a new device. The automation enables the broader clinical engineering team to focus more on detailed elements of the anatomy, along with analyzing and planning the procedure, rather than on the labor-intensive process of segmentation. Next, engineers precisely plan and simulate device placement, helping to ensure optimal outcomes even in the most challenging scenarios.

One illustrative case involved a patient with a failed valve that had embolized into the aortic arch, requiring a replacement valve.

“The challenge was to deploy our valve within this constrained anatomy without compromising hemodynamics or coronary flow,” explained the cardiac clinical engineer. “Using Mimics, the team created a detailed 3D model to visualize and plan the procedure, then 3D printed the anatomy to test the valve deployment physically. This meticulous planning ensures that the valve is positioned correctly, avoiding coronary obstruction and enabling good leaflet function. Using Mimics, the cardiac team has been able to ramp up from one to two cases a month to 10 a week.”

An essential aspect of this device design workflow is the feedback loop in which clinical information from the procedure is provided back to the clinical engineering team. This continuous knowledge stream helps the team refine their models and simulations, ensuring that future cases benefit from the lessons learned. By aggregating data from various cases, the team can identify patterns and improve their devices' design and deployment strategies. This real-time clinical insight is invaluable for ongoing R&D efforts, with the goal of making the devices more effective and safer for patients.