Papers
5
Total Citations
48
H-Index
4
About
Lennart Karstensen is an emerging researcher at the forefront of autonomous endovascular robotics, with a focused body of work exploring how artificial intelligence can transform minimally invasive vascular surgery. His research centers on applying reinforcement learning and recurrent neural networks to automate the navigation of catheters and guidewires through complex vascular anatomy — a technically demanding challenge with profound clinical implications. Karstensen's most-cited work (19 citations) demonstrates the viability of inverse reinforcement learning for autonomous catheter navigation in mechanical thrombectomy, a critical stroke intervention where speed and precision are paramount. His subsequent research on recurrent neural networks (17 citations) advances generalization across variable vessel geometries, addressing a key barrier to real-world deployment. Recognizing the need for rigorous evaluation, he has also contributed standardized benchmarking frameworks and ROS2-based testbed environments, providing the research community with reproducible tools for comparing and validating autonomous systems. Collectively, his work addresses pressing clinical challenges — operator radiation exposure, geographic inequity in specialist care, and procedural variability — by positioning intelligent robotics as a practical solution. Though early in his career, Karstensen's contributions are already shaping the methodological foundations of autonomous endovascular intervention research.
Research Focus
Key Achievements
Top Papers
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- 4A ROS2-based Testbed Environment for Endovascular Robotic Systems4 citations · 2022
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