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Intelligent laparoscopic grasper with hybrid neural networks for real-time vascular detection in minimally invasive
Pingping Wang1, Yuting Huang1, Chang Liu2
1Zhujiang Hospital, Southern Medical University, Obstetrics and Gynecology Center, Department of Gynecology, Guangzhou, China.
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 ( ). The CNN-LSTM algorithm achieved a precision of 97.06% in vessel identification across different tissue coverages.
Conclusions:
The FBG sensor-integrated laparoscopic grasper, assisted by the processing of the CNN-LSTM algorithm, demonstrated the ability to identify vessels ex vivo across different models. This technology holds potential for real-time and accurate vessel identification during MIS, which could significantly reduce the occurrence of unnecessary vessel injuries.
