Intelligent laparoscopic grasper with hybrid neural networks for real-time vascular detection in minimally invasive surgery.

Significance: We address the challenge of inadequate force feedback in laparoscopic surgery, which increases the risk of vessel injury. By integrating fiber Bragg grating (FBG) sensors with a laparoscopic grasper and employing a convolutional neural network combined with long short-term memory (CNN-LSTM) algorithm, this approach enables real-time, accurate vessel identification, potentially reducing surgical complications. Aim: Laparoscopic surgery is often hindered by inadequate force feedback, especially in complex scenarios involving tumor invasion and pelvic-abdominal adhesion, leading to challenges in locating blood vessels and an increased risk of vessel injury. Thus, it is desirable to develop a laparoscopic system capable of distinguishing the location and type of the vessels during surgery, which requires a compact and highly sensitive sensor integrated with a laparoscopic grasper. Approach: We present an innovative laparoscopic grasper integrated with FBG force sensors for real-time force feedback, employing silicone and porcine vessel models to simulate varying depths and tissue coverage. The device successfully captured specific vessel signals, which were processed through a CNN-LSTM algorithm, enabling real-time vessel identification in minimally invasive surgery (MIS). Results: The intelligent laparoscopic grasper successfully obtained distinct vessel signals under varying conditions. As a result, the mean vessel gripping force for porcine vessel model III was 0.059 N under fatty tissue and 0.032 N under muscle tissue ( Conclusions: The FBG sensor-integrated laparoscopic grasper, assisted by the processing of the CNN-LSTM algorithm, demonstrated the ability to identify vessels