Abstract
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…
Xiaobin Xiong 熊晓滨
Assistant Professor
Prof. Xiong is a full-stack roboticist who develop rigorous theories for practical applications.