  Spinal fusion surgery requires highly accurate implantation of pedicle screw
implants, which must be conducted in critical proximity to vital structures
with a limited view of anatomy. Robotic surgery systems have been proposed to
improve placement accuracy, however, state-of-the-art systems suffer from the
limitations of open-loop approaches, as they follow traditional concepts of
preoperative planning and intraoperative registration, without real-time
recalculation of the surgical plan. In this paper, we propose an intraoperative
planning approach for robotic spine surgery that leverages real-time
observation for drill path planning based on Safe Deep Reinforcement Learning
(DRL). The main contributions of our method are (1) the capability to guarantee
safe actions by introducing an uncertainty-aware distance-based safety filter;
and (2) the ability to compensate for incomplete intraoperative anatomical
information, by encoding a-priori knowledge about anatomical structures with a
network pre-trained on high-fidelity anatomical models. Planning quality was
assessed by quantitative comparison with the gold standard (GS) drill planning.
In experiments with 5 models derived from real magnetic resonance imaging (MRI)
data, our approach was capable of achieving 90% bone penetration with respect
to the GS while satisfying safety requirements, even under observation and
motion uncertainty. To the best of our knowledge, our approach is the first
safe DRL approach focusing on orthopedic surgeries.
