Fabio Carrillo
Papers
5
Total Citations
56
H-Index
4
About
Fabio Carrillo is a leading researcher at the intersection of robotic surgery, medical imaging, and artificial intelligence, with a primary focus on advancing spinal fusion procedures. His work addresses the critical challenge of pedicle screw placement, where millimeter-level accuracy is required near vital structures. Carrillo’s major contributions include pioneering robot-assisted ultrasound (US) systems that provide non-radiative 3D reconstructions of the spine, offering a safer alternative to traditional fluoroscopy. His 2023 study, "Robot-assisted ultrasound reconstruction for spine surgery: from bench-top to pre-clinical study," has garnered 26 citations, demonstrating its impact on the field. He also developed SafeRPlan, a safe deep reinforcement learning framework for intraoperative planning of screw trajectories, which has been cited 16 times. This work directly tackles the limitations of current robotic systems by integrating safety constraints into AI-driven planning. Additionally, his ex-vivo animal validation studies have provided crucial preclinical evidence for the feasibility of US-guided robotic navigation. Carrillo’s research is notable for bridging the gap between bench-top development and clinical translation, with a strong emphasis on reducing radiation exposure and improving surgical precision. His achievements position him as a key innovator in non-radiative, AI-enhanced robotic spine surgery.
Research Focus
Key Achievements
Top Papers
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- 5Safe Deep RL for Intraoperative Planning of Pedicle Screw Placement2 citations · 2023