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.

翻译：脊柱融合手术需要高精度地植入椎弓根螺钉，且必须在解剖结构视野受限、邻近重要组织的关键区域进行。机器人手术系统已被提出以提高植入精度，然而，现有最先进系统受限于开环方法的缺陷，遵循术前规划与术中配准的传统理念，无法实时重新计算手术方案。本文提出一种基于安全深度强化学习的机器人脊柱手术术中规划方法，利用实时观测进行钻孔路径规划。该方法的主要创新点包括：（1）通过引入基于不确定性感知的距离安全滤波器，保障手术动作的安全性；（2）通过在高保真解剖模型上预训练的网络编码解剖结构先验知识，补偿术中解剖信息不完整的问题。通过与金标准钻孔规划进行定量对比评估规划质量，在基于真实磁共振成像数据构建的5个模型实验中，该方法在满足安全性要求的前提下，即使面临观测与运动不确定性，仍能实现金标准90%的骨骼穿透率。据我们所知，该方法是首个专注于骨科手术的安全深度强化学习方法。