Exploring the Limitations and Implications of the JIGSAWS Dataset for Robot-Assisted Surgery

Abstract

The JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS) dataset has proven to be a foundational component of modern work on the skill analysis of robotic surgeons. In particular, methods using either the system's kinematics or video data have shown to be able to classify operators into distinct experience levels, and recent approaches have even ventured to recover numeric skill ratings assigned to assessment sessions. Although prior works have achieved positive results in these directions, challenges still remain with classification across all three levels of operator training amounts and objective skill rating regressions. To this end, we perform the first statistical analysis of the dataset itself and compile the results here. We find limited relationships between the amount of experience or training of an operator and their performance in JIGSAWS. Moreover, as operator-side kinematics have well-known relationships with their skill, previous works have used both robot and operator-side kinematics to classify operator skill; we find the first explicit relationships between pure robot-side kinematics and surgical performance. Finally, we analyze the robotic kinematic trends associated with high performance in JIGSAWS tasks and present how they may be used as indicators in human and automated surgeon training.