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Many tasks in robot-assisted surgeries (RAS) can be represented byfinite-state machines (FSMs), where each state represents either an action(such as picking up a needle) or an observation (such as bleeding). A crucialstep towards the automation of such surgical tasks is the temporal perceptionof the current surgical scene, which requires a real-time estimation of thestates in the FSMs. The objective of this work is to estimate the current stateof the surgical task based on the actions performed or events occurred as thetask progresses. We propose Fusion-KVE, a unified surgical state estimationmodel that incorporates multiple data sources including the Kinematics, Vision,and system Events. Additionally, we examine the strengths and weaknesses ofdifferent state estimation models in segmenting states with differentrepresentative features or levels of granularity. We evaluate our model on theJHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), as well as a morecomplex dataset involving robotic intra-operative ultrasound (RIOUS) imaging,created using the da Vinci Xi surgical system. Our model achieves a superiorframe-wise state estimation accuracy up to 89.4%, which improves thestate-of-the-art surgical state estimation models in both JIGSAWS suturingdataset and our RIOUS dataset.

Temporal Segmentation of Surgical Sub-tasks through Deep Learning with Multiple Data Sources