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

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.