An automated skills assessment framework for laparoscopic training tasks

Background: Various sensors and methods are used for evaluating trainees' skills in laparoscopic procedures. These methods are usually task-specific and involve high costs or advanced setups. Methods: In this paper, we propose a novel manoeuver representation feature space (MRFS) constructed by tracking the vanishing points of the edges of the graspers on the video sequence frames, acquired by the standard box trainer camera. This study aims to provide task-agnostic classification of trainees in experts and novices using a single MRFS over two basic laparoscopic tasks. Results: The system achieves an average of 96% correct classification ratio (CCR) when no information on the performed task is available and >98% CCR when the task is known, outperforming a recently proposed video-based technique by >13%. Conclusions: Robustness, extensibility and accurate task-agnostic classification between novices and experts is achieved by utilizing advanced computer vision techniques and derived features from a novel MRFS.