  Robotic-assisted surgical systems have demonstrated significant potential in
enhancing surgical precision and minimizing human errors. However, existing
systems lack the ability to accommodate the unique preferences and requirements
of individual surgeons. Additionally, they primarily focus on general surgeries
(e.g., laparoscopy) and are not suitable for highly precise microsurgeries,
such as ophthalmic procedures. Thus, we propose a simulation-based image-guided
approach for surgeon-centered autonomous agents that can adapt to the
individual surgeon's skill level and preferred surgical techniques during
ophthalmic cataract surgery. Our approach utilizes a simulated environment to
train reinforcement and imitation learning agents guided by image data to
perform all tasks of the incision phase of cataract surgery. By integrating the
surgeon's actions and preferences into the training process with the
surgeon-in-the-loop, our approach enables the robot to implicitly learn and
adapt to the individual surgeon's unique approach through demonstrations. This
results in a more intuitive and personalized surgical experience for the
surgeon. Simultaneously, it ensures consistent performance for the autonomous
robotic apprentice. We define and evaluate the effectiveness of our approach
using our proposed metrics; and highlight the trade-off between a generic agent
and a surgeon-centered adapted agent. Moreover, our approach has the potential
to extend to other ophthalmic surgical procedures, opening the door to a new
generation of surgeon-in-the-loop autonomous surgical robots. We provide an
open-source simulation framework for future development and reproducibility.
