A sensorized modular training platform to reduce vascular damage in endovascular surgery
Fischer N, Marzi C, Meisenbacher K, Kisilenko A, Davitashvili T, Wagner M, Mathis-Ullrich F (2023)
Publication Type: Journal article
Publication year: 2023
Journal
DOI: 10.1007/s11548-023-02935-w
Abstract
Purpose: Endovascular interventions require intense practice to develop sufficient dexterity in catheter handling within the human body. Therefore, we present a modular training platform, featuring 3D-printed vessel phantoms with patient-specific anatomy and integrated piezoresistive impact force sensing of instrument interaction at clinically relevant locations for feedback-based skill training to detect and reduce damage to the delicate vascular wall. Methods: The platform was fabricated and then evaluated in a user study by medical (n= 10) and non-medical (n= 10) users. The users had to navigate a set of guidewire and catheter through a parkour of 3 modules including an aneurismatic abdominal aorta, while impact force and completion time were recorded. Eventually, a questionnaire was conducted. Results: The platform allowed to perform more than 100 runs in which it proved capable to distinguish between users of different experience levels. Medical experts in the fields of vascular and visceral surgery had a strong performance assessment on the platform. It could be shown, that medical students could improve runtime and impact over 5 runs. The platform was well received and rated as promising for medical education despite the experience of higher friction compared to real human vessels. Conclusion: We investigated an authentic patient-specific training platform with integrated sensor-based feedback functionality for individual skill training in endovascular surgery. The presented method for phantom manufacturing is easily applicable to arbitrary patient-individual imaging data. Further work shall address the implementation of smaller vessel branches, as well as real-time feedback and camera imaging for further improved training experience.
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Germany (DE)How to cite
APA:
Fischer, N., Marzi, C., Meisenbacher, K., Kisilenko, A., Davitashvili, T., Wagner, M., & Mathis-Ullrich, F. (2023). A sensorized modular training platform to reduce vascular damage in endovascular surgery. International Journal of Computer Assisted Radiology and Surgery. https://doi.org/10.1007/s11548-023-02935-w
MLA:
Fischer, Nikola, et al. "A sensorized modular training platform to reduce vascular damage in endovascular surgery." International Journal of Computer Assisted Radiology and Surgery (2023).
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