An Interventional Surgical Robot Based on Multi-Data Detection

Abstract

Vascular interventional surgery is the most common method for the treatment of cardiovascular diseases. Interventional surgical robot has attracted extensive attention because of its precise control and remote operation. However, conventional force sensors in surgical robots can only detect the axial thrust pressure of the catheter. Inspired by the function of insect antennae, we designed a structure with a thin-film force sensing device in the catheter head. Combined with the pressure sensor in the catheter clamping device, multiple sensor data were fused to predict and classify the current vascular environment using the LSTM network with 94.2% accuracy. During robotic surgery, real-time feedback of current pressure information and vascular curvature information can enhance doctors’ judgment of surgical status and improve surgical safety.