Purpose: Most evaluations of surgical workflow or surgeon skill use simple, descriptive statistics (e.g., time) across whole procedures, thereby deemphasizing critical steps and potentially obscuring critical inefficiencies or skill deficiencies. In this work, we examine off-line, temporal clustering methods that chunk training procedures into clinically relevant surgical tasks or steps during robot-assisted surgery.
Methods: We collected system kinematics and events data from nine surgeons performing five different surgical tasks on a porcine model using the da Vinci Si surgical system. The five tasks were treated as one 'pseudo-procedure.' We compared four different temporal clustering algorithms-hierarchical aligned cluster analysis (HACA), aligned cluster analysis (ACA), spectral clustering (SC), and Gaussian mixture model (GMM)-using multiple feature sets.
Results: HACA outperformed the other methods reaching an average segmentation accuracy of [Formula: see text] when using all system kinematics and events data as features. SC and ACA reached moderate performance with [Formula: see text] and [Formula: see text] average segmentation accuracy, respectively. GMM consistently performed poorest across algorithms.
Conclusions: Unsupervised temporal segmentation of surgical procedures into clinically relevant steps achieves good accuracy using just system data. Such methods will enable surgeons to receive directed feedback on individual surgical tasks rather than whole procedures in order to improve workflow, assessment, and training.
Keywords: Clustering; Performance evaluation; Robot-assisted surgery; Segmentation.