Guidewire feeding method based on deep reinforcement learning for vascular intervention robot

Vascular interventional surgery has the advantages of small trauma, rapid postoperative recovery, and a high safety factor. It is widely used in the treatment of various cardiovascular diseases. In order to reduce the X-ray radiation exposure to doctors, the existing vascular interventional surgery robot is mainly operated by human teleoperation. Currently, how to achieve autonomously intelligent guidewire feeding has become an attractive issue for researchers. This paper proposes an autonomous wire feeding method in interventional surgery based on the deep reinforcement learning framework SAC (Soft Actor-Critic). We adopt the target detection algorithm YOLOv5s to obtain the guidewire tip, which is used for state update of the SAC agent. The guidewire feeding experiments on the vessel phantom verified that the deep reinforcement learning SAC method could well complete the guidewire feeding task in interventional surgery. Further research will utilize the combination of guidewire and guide catheter to solve more complex surgical tasks. The experiment results show that guidewire end detection accuracy can reach at least 92.9% and the guidewire feeding accuracy is less than 5mm.