Sara Zucchelli
Papers
2
Total Citations
59
H-Index
2
About
Sara Zucchelli is a leading researcher in robot-assisted keyhole neurosurgery, specializing in the intersection of medical robotics, deformable tissue simulation, and machine learning for surgical planning. Her major contributions center on developing intelligent frameworks for flexible neurosurgical robots, enabling safer and more effective minimally invasive brain procedures. Her most cited work, "Inverse Reinforcement Learning Intra-Operative Path Planning for Steerable Needle" (2021, 40 citations), introduces a novel planning framework that learns optimal needle trajectories from expert demonstrations, significantly enhancing precision during keyhole surgery. Complementing this, her study "Position-Based Dynamics Simulator of Brain Deformations for Path Planning and Intra-Operative Control in Keyhole Neurosurgery" (2021, 19 citations) presents a realistic, time-bounded simulator using Position-Based Dynamics to model brain deformations during catheter insertion. This work provides a critical tool for pre-operative planning and real-time control, addressing the challenge of navigating highly deformable brain tissue. Zucchelli’s research has direct clinical impact, offering neurosurgeons advanced support systems that reduce risk and improve outcomes in complex procedures. Her innovative integration of inverse reinforcement learning with surgical robotics positions her as a key figure in the future of autonomous and semi-autonomous neurosurgical systems.
Research Focus
Key Achievements
Top Papers
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