MEDiC: Autonomous Surgical Robotic Assistance to Maximizing Exposure for Dissection and Cautery

Xiao Liang, Chung-Pang Wang, Nikhil Uday Shinde, Fei Liu, Florian Richter, Michael C. Yip

Abstract:

Surgical automation has the capability to improve
the consistency of patient outcomes and broaden access to
advanced surgical care in underprivileged communities. Shared
autonomy, where the robot automates routine subtasks while
the surgeon retains partial teleoperative control, offers great
potential to make an impact. In this paper we focus on one
important skill within surgical shared autonomy: Automating
robotic assistance to maximize visual exposure and apply tissue
tension for dissection and cautery. Ensuring consistent exposure
to visualize the surgical site is crucial for both efficiency and patient safety. However, achieving this is highly challenging due to
the complexities of manipulating deformable volumetric tissues
that are prevalent in surgery. To address these challenges we
propose MEDiC, a framework for autonomous surgical robotic
assistance to Maximizing Exposure for Dissection and Cautery.
We integrate a differentiable physics model with perceptual
feedback to achieve our two key objectives: 1) Maximizing
tissue exposure and applying tension for a specified dissection
site through visual-servoing conrol and 2) Selecting optimal
control positions for a dissection target based on deformable
Jacobian analysis. We quantitatively assess our method through
repeated real robot experiments on a tissue phantom, and
showcase its capabilities through dissection experiments using
shared autonomy on real animal tissue.