Yunning Wang
Papers
1
Total Citations
3
H-Index
1
About
Yunning Wang is a leading researcher in the field of surgical robotics, with a primary focus on enhancing the safety and precision of robotic-assisted spinal procedures. Their work centers on the integration of advanced sensing technologies and deep learning models to enable real-time tissue state identification during operations. Wang’s most notable contribution is the development of a breakthrough prediction system for robotic laminectomy, which uses electrical impedance monitoring combined with a Long Short-Term Memory Fully Convolutional Network (LSTM-FCN) to accurately detect when the ultrasonic osteotome is about to cut through bone. This innovation directly addresses a critical safety challenge in spine surgery, where inadvertent damage to soft tissue can lead to severe complications. Although their seminal 2022 paper has garnered 3 citations to date, the work represents a foundational step toward autonomous, context-aware surgical tools. Wang’s research is particularly impactful for the growing field of ultrasonic osteotomy, demonstrating how machine learning can transform raw sensor data into actionable intraoperative guidance, ultimately paving the way for safer, more reliable robotic surgery.
Research Focus
Key Achievements
Top Papers
- 1