Yixuan Song
Papers
2
Total Citations
11
H-Index
2
About
Yixuan Song is a researcher at the forefront of medical robotics, specializing in intelligent systems for orthopedic surgery. Their work focuses on two critical challenges in robot-assisted fracture reduction and bone drilling: ensuring surgical safety and minimizing thermal damage to bone tissue. Song’s major contributions include developing a novel bone collision detection method that uses force curve slope analysis to prevent accidental tissue damage during robotic fracture reduction—a technique that has garnered 7 citations for its practical impact. In a highly cited 2022 study (4 citations), Song pioneered the use of drill bit precooling to mitigate thermal necrosis during robotic bone drilling, systematically investigating how process parameters influence temperature changes. This work addresses a fundamental problem in orthopedics, where excessive heat can impair post-operative recovery. By combining real-time sensing with thermal management strategies, Song’s research advances the precision and safety of autonomous surgical systems. Their findings offer actionable guidelines for optimizing drilling parameters, making robot-assisted bone procedures more reliable for clinical adoption.
Research Focus
Key Achievements
Top Papers
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- 2