Lens Capsule Tearing in Cataract Surgery using Reinforcement Learning
Rebekka Charlotte Peter, Steffen Peikert, Ludwig Haide, Doan Xuan Viet Pham, Tahar Chettaoui, Eleonora Tagliabue, Paul Maria Scheikl, Johannes Fauser
Abstract
Cataract is the leading cause of blindness worldwide with an increasing number of patients due to changing demographics, making automation an important part in future surgical treatment. In this work, we focus on a substep of cataract surgery, the Continuous Curvilinear Capsulorhexis (CCC). With a high complexity, this task is an ideal candidate for Reinforcement Learning (RL) in simulation. First, we present an interactive and physically realistic simulation based on the Finite Element Method (FEM) that mimics the tearing behavior of soft tissue during CCC. Then, we train and evaluate RL models in simulation, demonstrating that the trained policies can complete the CCC in 85% of cases. We also show that applying domain randomization techniques make the policy more robust against changes in geometrical and biomechanical boundary conditions.
BibTeX
@inproceedings{icra2024_lenscapsuleteari,
title = {Lens Capsule Tearing in Cataract Surgery using Reinforcement Learning},
author = {Rebekka Charlotte Peter and Steffen Peikert and Ludwig Haide and Doan Xuan Viet Pham and Tahar Chettaoui and Eleonora Tagliabue and Paul Maria Scheikl and Johannes Fauser and Matthias Hillenbrand and Gerhard Neumann and Franziska Mathis-Ullrich},
booktitle = {ICRA 2024},
year = {2024}
}