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.

本文提出了一种基于实时观察的机器人脊柱手术规划方法，利用 Safe Deep Reinforcement Learning（DRL）计算钻孔路径，同时通过引入一个基于距离的不确定性感知安全过滤器保证安全，为手术提供尽可能高的成功率。实验表明，该方法是第一种在正畸手术上应用安全 DRL 方法，且能同时满足成功率和安全保障。