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：脊柱融合手术要求椎弓根螺钉植入物具有高度精确性，且必须在解剖结构视野受限且紧邻关键组织的情况下进行。机器人手术系统已被提出用于提高置入精度，然而现有系统受限于开环方法，其遵循术前规划与术中配准的传统概念，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，通过实时观测引导钻孔路径规划。该方法的主要贡献包括：(1) 引入基于不确定性感知的距离安全滤波器，实现安全动作的保障；(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，补偿术中解剖信息的不完整性。通过定量对比金标准钻孔规划评估规划质量，在基于真实磁共振成像数据生成的5个模型实验中，本方法即使在观测与运动不确定性条件下，仍能实现相对于金标准90%的骨穿透率且满足安全要求。据我们所知，本方法是首个聚焦骨科手术的安全深度强化学习方法。