Youxinag Li

Papers

Total Citations

H-Index

About

Youxiang Li is a leading researcher in the field of surgical robotics and medical image computing, with a primary focus on developing intelligent systems for minimally invasive procedures. His most impactful work centers on the integration of deep learning and computer vision to enhance robotic surgical navigation. In his seminal 2019 paper, "A CNN-based prototype method of unstructured surgical state perception and navigation for an endovascular surgery robot," Li pioneered a convolutional neural network approach that enables robots to perceive and navigate complex, unstructured surgical environments in real time. This contribution, which has garnered 88 citations, addresses a critical challenge in endovascular surgery: the need for autonomous or semi-autonomous systems that can adapt to unpredictable anatomical conditions. By bridging the gap between raw sensor data and actionable surgical state understanding, Li’s work has laid the foundation for more precise, safer robotic interventions. His research not only advances the technical capabilities of surgical robots but also promises to reduce procedural risks and improve patient outcomes. Li’s achievements are recognized as pivotal in the ongoing evolution of intelligent surgical systems, making him a key figure in the intersection of robotics, artificial intelligence, and clinical medicine.