Robotic Surgery
Robotic surgery aims to improve surgical precision, minimally invasiveness, and efficiency through robotic systems. Current research heavily focuses on enhancing the surgeon's experience by developing advanced haptic feedback, automated skill assessment using machine learning models (like deep learning networks and transformers), and improved 3D scene reconstruction for telemedicine applications. These advancements leverage techniques such as deep reinforcement learning, large language models, and advanced computer vision algorithms to improve surgical planning, execution, and training, ultimately leading to better patient outcomes and more efficient surgical workflows.
Papers
SAM 2 in Robotic Surgery: An Empirical Evaluation for Robustness and Generalization in Surgical Video Segmentation
Jieming Yu, An Wang, Wenzhen Dong, Mengya Xu, Mobarakol Islam, Jie Wang, Long Bai, Hongliang Ren
A Review of 3D Reconstruction Techniques for Deformable Tissues in Robotic Surgery
Mengya Xu, Ziqi Guo, An Wang, Long Bai, Hongliang Ren
Improving the realism of robotic surgery simulation through injection of learning-based estimated errors
Juan Antonio Barragan, Hisashi Ishida, Adnan Munawar, Peter Kazanzides
Realistic Data Generation for 6D Pose Estimation of Surgical Instruments
Juan Antonio Barragan, Jintan Zhang, Haoying Zhou, Adnan Munawar, Peter Kazanzides