Shibo Li

About

Shibo Li is a pioneering researcher at the intersection of robotics and surgical automation, with a primary focus on robot-assisted endoscopic nasal surgery, orthopedic trauma reduction, and spinal interventions. His major contributions include developing intelligent path planning algorithms that enhance the safety and precision of surgical robots. In endoscopic nasal surgery, Li introduced methods for automatic field-of-view control and collision-free endoscope navigation through the complex nasal cavity, directly addressing the risk of tissue injury during robot-assisted procedures. His work on computer-assisted preoperative planning for pelvic fracture reduction, leveraging an enlarged RRT* algorithm, and on force-position hybrid compensation for bone drilling path deviation, demonstrates his commitment to solving real-world surgical challenges. With over 66 citations across his most-cited works, Li’s research has gained traction for its practical impact on reducing surgeon burden and improving patient outcomes. Notably, his spinal canal generation method using generative adversarial networks for CT image inpainting highlights his innovative use of AI in surgical planning. Li’s contributions are shaping the next generation of safer, more autonomous surgical robotic systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Endoscopic Path Planning in Robot-Assisted Endoscopic Nasal Surgery

   27 citations · 2020
2. 2
   Automatic Surgical Field of View Control in Robot-Assisted Nasal Surgery

   25 citations · 2020
3. 3
   Computer-Assisted Automatic Preoperative Path Planning Method for Pelvic Fracture Reduction Surgery Based on Enlarged RRT* Algorithm

   6 citations · 2024
4. 4
   Research on Spinal Canal Generation Method based on Vertebral Foramina Inpainting of Spinal CT Images by using BEGAN

   5 citations · 2020
5. 5
   Force-Position Hybrid Compensation Control for Path Deviation in Robot-Assisted Bone Drilling

   3 citations · 2023

Key Collaborators

Contact & Links