Fusion-KVE, a unified surgical state estimation model incorporating kinematics, vision, and system events, achieved a superior frame-wise state estimation accuracy of up to 89.4%.
The proposed Fusion-KVE deep learning model improves real-time surgical state estimation accuracy up to 89.4% by integrating kinematics, vision, and system events.
Many tasks in robot-assisted surgeries (RAS) can be represented by finite-state machines (FSMs), where each state represents either an action (such as picking up a needle) or an observation (such as bleeding). A crucial step towards the automation of such surgical tasks is the temporal perception of the current surgical scene, which requires a real-time estimation of the states in the FSMs. The objective of this work is to estimate the current state of the surgical task based on the actions performed or events occurred as the task progresses. We propose Fusion-KVE, a unified surgical state estimation model that incorporates multiple data sources including the Kinematics, Vision, and system Events. Additionally, we examine the strengths and weaknesses of different state estimation models in segmenting states with different representative features or levels of granularity. We evaluate our model on the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), as well as a more complex dataset involving robotic intra-operative ultrasound (RIOUS) imaging, created using the da Vinci® Xi surgical system. Our model achieves a superior frame-wise state estimation accuracy up to 89.4%, which improves the state-of-the-art surgical state estimation models in both JIGSAWS suturing dataset and our RIOUS dataset.
Qin et al. (Fri,) conducted a other in Robot-assisted surgeries. Fusion-KVE model vs. State-of-the-art surgical state estimation models was evaluated on Frame-wise state estimation accuracy. Fusion-KVE, a unified surgical state estimation model incorporating kinematics, vision, and system events, achieved a superior frame-wise state estimation accuracy of up to 89.4%.