(untitled) (Transactions on Additive Manufacturing Meets Medicine, 2024)
Endovascular treatment of abdominal aortic aneurysms, specifically through Endovascular Aneurysm Repair (EVAR), presents a complex and challenging procedure requiring precise technical skills and accurate interpretation of imaging. Realistic training models are essential for preparing new clinicians. This study presents the development and evaluation of a modular EVAR training model intended for future integration into the Hamburg Anatomical Neurointerventional Simulator (HANNES). Aimed at providing realistic and effective training using original treatment instruments, the model’s design and manufacturing process included the development and evaluation of several concepts, ensuring modularity and reusability with potential for future expansion to accommodate additional aneurysm geometries. An interdisciplinary team of engineers and medical professionals conducted the requirement definition and documentation. The resulting EVAR model features a three-dimensional vascular tree designed to accommodate treatment instruments and provide a realistic simulation environment. Additive manufacturing were used to manufacture the complex geometries of the model, with materials chosen for their flexibility and X-ray compatibility. Experimental validation by an experienced vascular surgeon using an angiography system demonstrated the model’s capability to replicate real procedural conditions. Despite some issues with vascular wall friction and component durability, the overall feedback was positive. The model realistically depicted necessary vascular sections and facilitated successful placement and removal of the stent graft. However, the high friction of the vascular wall material indicated a need for further material optimization to prevent intraoperative tears. Future iterations will focus on reducing friction and integrating the EVAR model into HANNES. By achieving these improvements, we aim to create a versatile simulator that enhances medical education across multiple disciplines, ultimately contributing to improved clinical outcomes.