Towards automatic skill evaluation: detection and segmentation of robot-assisted surgical motions

Abstract

This paper reports our progress in developing techniques for "parsing" raw motion data from a simple surgical task into a labeled sequence of surgical gestures. The ability to automatically detect and segment surgical motion can be useful in evaluating surgical skill, providing surgical training feedback, or documenting essential aspects of a procedure. If processed online, the information can be used to provide context-specific information or motion enhancements to the surgeon. However, in every case, the key step is to relate recorded motion data to a model of the procedure being performed. Robotic surgical systems such as the da Vinci system from Intuitive Surgical provide a rich source of motion and video data from surgical procedures. The application programming interface (API) of the da Vinci outputs 192 kinematics values at 10 Hz. Through a series of feature-processing steps, tailored to this task, the highly redundant features are projected to a compact and discriminative space. The resulting classifier is simple and effective.Cross-validation experiments show that the proposed approach can achieve accuracies higher than 90% when segmenting gestures in a 4-throw suturing task, for both expert and intermediate surgeons. These preliminary results suggest that gesture-specific features can be extracted to provide highly accurate surgical skill evaluation.