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Updated: Jul 30, 2025
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Simulator Training for Endovascular Neurosurgery
Published on: May 6, 2020
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A sensorized modular training platform to reduce vascular damage in endovascular surgery.
Nikola Fischer1, Christian Marzi1, Katrin Meisenbacher2
1Health Robotics and Automation, Karlsruhe Institute of Technology, Institute for Anthropomatics and Robotics, 76131, Karlsruhe, Germany.
Summary
This study introduces a 3D-printed training platform with sensors for endovascular interventions. The system provides feedback on instrument interaction, helping surgeons improve catheter handling skills and reduce vascular damage.
Area of Science:
- Biomedical Engineering
- Medical Simulation
- Surgical Training
Background:
- Endovascular interventions demand extensive practice for proficiency in catheter manipulation.
- Developing surgical skills requires realistic training environments to minimize patient risk.
Purpose of the Study:
- To present a modular training platform with patient-specific 3D-printed vascular phantoms.
- To integrate piezoresistive impact force sensing for feedback-based skill development.
- To enable detection and reduction of vascular wall damage during training.
Main Methods:
- Fabrication of a modular training platform with 3D-printed vessel phantoms.
- User study involving medical and non-medical participants navigating simulated anatomy.
- Recording of impact forces and completion times during guidewire and catheter navigation.
Main Results:
- The platform successfully distinguished between users of varying experience levels.
- Medical experts demonstrated strong performance, and medical students improved over training runs.
- Users rated the platform as promising for medical education, despite higher friction than real vessels.
Conclusions:
- An authentic, patient-specific training platform with sensor-based feedback for endovascular surgery was developed.
- The phantom manufacturing method is adaptable to diverse patient imaging data.
- Future work will focus on incorporating smaller vessel branches and real-time imaging feedback.
