Yao Zhang
Papers
3
Total Citations
129
H-Index
3
About
Yao Zhang is a leading researcher at the intersection of robotics, machine learning, and minimally invasive surgery, with a primary focus on enhancing the precision and safety of robotic catheters for cardiovascular interventions. His major contributions lie in addressing critical challenges in catheter-based procedures, particularly hysteresis and compliant motion control. In his highly cited 2021 work (64 citations), Zhang pioneered the use of Long Short-Term Memory networks to model and compensate for hysteresis in robotic catheters, significantly improving tip positioning accuracy and reducing the risk of tissue damage. He further advanced the field with a deep-learning-based compliant motion control system for pneumatically-driven catheters (40 citations), enabling safer navigation by preventing excessive force on vessel walls. His 2024 comprehensive review (25 citations) on machine learning in flexible surgical robots has become a key reference, mapping the current landscape and future directions of the field. Zhang’s work is notable for translating complex deep-learning techniques into practical, safety-critical applications, directly impacting the development of next-generation autonomous surgical systems.
Research Focus
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