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A sensorized modular training platform to reduce vascular damage in endovascular surgery

Fischer, Nikola ORCID iD icon 1,2; Marzi, Christian ORCID iD icon 2,3; Meisenbacher, Katrin; Kisilenko, Anna; Davitashvili, Tornike; Wagner, Martin; Mathis-Ullrich, Franziska 2
1 Institut für Telematik (TM), Karlsruher Institut für Technologie (KIT)
2 Institut für Anthropomatik und Robotik (IAR), Karlsruher Institut für Technologie (KIT)
3 Fakultät für Informatik – Institut für Prozessrechentechnik, Automation und Robotik (IPR), Karlsruher Institut für Technologie (KIT)

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. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000158818
Veröffentlicht am 19.05.2023
Originalveröffentlichung
DOI: 10.1007/s11548-023-02935-w
Scopus
Zitationen: 1
Dimensions
Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Institut für Telematik (TM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2023
Sprache Englisch
Identifikator ISSN: 1861-6429
KITopen-ID: 1000158818
Erschienen in International Journal of Computer Assisted Radiology and Surgery
Verlag Springer Verlag
Vorab online veröffentlicht am 17.05.2023
Schlagwörter Sensorized phantom, Impact force sensing, Piezoresistive sensing, Endovascular training
Nachgewiesen in Dimensions
Scopus
Web of Science
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