A cognitive path-guidance-system for minimally invasive surgery

The presented path-guidance system is able to learn movements and to predict motion. It shall enhance safe navigation for surgeons in minimally invasive surgery by creating a virtual fixture which holds the end-effector's motion to a desired path and warning the surgeon in a dangerous situation. Surgeons can demonstrate interventions and best practices. The system collects information from surgeon demonstrated trajectories, defined as best practices, and extracts knowledge to provide guidance for other users to carry out the same intervention. Knowledge extraction is achieved through trajectory clustering, maximum likelihood classification and a Markov model to predict states. The fundamental task is to guide a surgeon along a desired trajectory (navigated path) and prevent them entering into zones of risk. The path is not sequential, furcations are permitted and modeled showing alternatives in the ongoing intervention. An evaluation with a pelvitrainer showed good results with over 89% hit rate in predicting the motion.