ArticleInternational journal of computer assisted radiology and surgery2023
A sensorized modular training platform to reduce vascular damage in endovascular surgery.
Article in International journal of computer assisted radiology and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed, 5 citations in OpenAlex.
- A Position Statement on Endovascular Models and Effectiveness Metrics for Mechanical Thrombectomy Navigation, on Behalf of the Stakeholder Taskforce for Artificial Intelligence-Assisted Robotic Thrombectomy (START).Journal of the American Heart Association · 2026Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
purposeEndovascular 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.
methodsThe platform was fabricated and then evaluated in a user study by medical ([Formula: see text]) and non-medical ([Formula: see text]) 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.
resultsThe 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.
conclusionWe 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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Registered trials
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