Real-time trajectory prediction of laparoscopic instrument tip based on long short-term memory neural network in laparoscopic surgery training.

Abstract

To provide appropriate surgical training guidance, some skill evaluation and safety detection methods have been developed. However, these methods are difficult to provide predictive information for trainees. This paper proposes a new approach for real-time trajectory prediction of the laparoscopic instrument tip to improve surgical training and the patient safety. This paper proposes a real-time trajectory prediction model of laparoscopic instrument tip based on long short-term memory (LSTM) neural network. Meanwhile, motion state is introduced to capture more motion information of the instrument tip and improve the model performance. The feasibility, effectiveness and generalisation ability of this proposed model are preliminarily verified. The model shows satisfactory prediction accuracy for the trajectory of the laparoscopic instrument tip. LSTM neural network can accurately predict the movement trajectory of the laparoscopic instrument tip. The prediction model can play a critical role in operational risk perception in advance, which can be used in laparoscopic surgery training.