Qiaodi Yuan
Papers
1
Total Citations
32
H-Index
1
About
Qiaodi Yuan’s research lies at the intersection of robotics, surgical technology, and machine learning, with a primary focus on advancing robot-assisted minimally invasive surgery (RMIS). Their most notable contribution is a novel algorithm that estimates tool-tissue forces during surgery without relying on external force sensors—a critical challenge in RMIS. By leveraging motor currents from surgical instruments and neural network methods, Yuan’s work enables safer, more precise tissue manipulation, reducing the risk of trauma during delicate procedures. This 2019 paper has garnered 32 citations, reflecting its impact on the field of surgical robotics and haptic feedback. Yuan’s approach bridges the gap between data-driven modeling and real-time clinical applications, offering a cost-effective and scalable solution for enhancing surgical outcomes. Their research not only advances autonomous surgical systems but also provides a foundation for future work in force estimation and human-robot interaction in medical settings. Through this work, Yuan demonstrates a commitment to improving patient safety and surgical precision, positioning themselves as a key contributor to the evolving landscape of intelligent surgical tools.
Research Focus
Key Achievements
Top Papers
- 1