Vascular Environment Identification Based on Multi-dimensional Data Fusion for Interventional Surgical Robots

Abstract

Vascular interventional surgery is the most commonly used method for the treatment of cardio-vascular and cerebrovascular diseases. Master-slave interventional surgical robot is a promising technology, which can further improve the accuracy and safety of surgery. However, imperfect measurement of catheter force remains a surgical risk. Inspired by the function of insect antennae, a thin-film force sensing device was installed in the catheter head. Combined with the pressure sensor in the catheter clamping device, the LSTM network was used to predict and classify the curvature of the current passing vessel, and the recognition accuracy was 97%. In the process of robotic surgery, real-time feedback of current pressure information and vascular curvature information can enhance the doctor's judgment of the operation state and improve the safety of surgery.