Vascular interventional doctors are exposed to radiation hazards during surgery and endure high work intensity. Remote vascular interventional surgery robotics is a hot research field, in which researchers aim to not only protect the health of interventional doctors, but to also improve surgical accuracy and efficiency. However, the current vascular interventional robots have numerous shortcomings, such as poor haptic feedback, few compatible surgeries and instruments, and cumbersome maintenance and operational procedures. Nevertheless, vascular interventional surgery combined with robotics provides more cutting?edge directions, such as Internet remote surgery combined with 5G network technology and the application of artificial intelligence in surgical procedures. To summarize the developmental status and key technical points of intravascular interventional surgical robotics research, we performed a systematic literature search to retrieve original articles related to remote vascular interventional surgery robotics published up to December 2020. This review, which includes 113 articles published in English, introduces the mechanical and structural characteristics of various aspects of vascular interventional surgical robotics, discusses the current key features of vascular interventional surgical robotics in force sensing, haptic feedback, and control methods, and summarizes current frontiers in autonomous surgery, long-distance robotic telesurgery, and magnetic resonance imaging (MRI)-compatible structures. On the basis of summarizing the current research status of remote vascular interventional surgery robotics, we aim to propose a variety of prospects for future robotic systems

Vascular intervention surgery offers advantages such as minimal invasiveness, quick recovery, and low side-effects. Achieving automatic guidewire navigation in interventional surgical robots can effectively assist doctors in performing the surgery. Achieving automated guidewire navigation for interventional surgical robots can effectively assist doctors in performing the surgery. Deep learning and reinforcement learning methods have been widely used for guidewire navigation tasks. However, there are challenges such as the simplicity of the simulated environment and the limited diversity of reward functions, which prevent the training results from demonstrating the intelligence required in more complex environments. Therefore, we propose a virtual training environment that incorporates real vascular projections to create a more complex environment. In this environmenzt, we introduce the distance between the guidewire tip and the target point into the reward function, utilize real-time images as input states, employ a multi-threaded Proximal Policy Optimization (PPO) algorithm to accelerate convergence, and adopt a multi-stage training approach that divides the navigation task into multiple sub-tasks to reduce task difficulty. The results demonstrate the effectiveness of our method in achieving automated guidewire navigation in the virtual environment, improving the success rate of guidewire navigation, and enhancing the algorithm’s robustness. Finally, by visualizing the attention locations of the neural network on the incoming images in the virtual environment, we process real-time images from the physical environment and transfer the trained model from the virtual environment to our physical interventional surgical robot, thus validating the feasibility of our method in real-world scenarios.