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 the anatomy. Robotic surgery systems have been proposed to improve placement accuracy. Despite remarkable advances, current robotic systems still lack advanced mechanisms for continuous updating of surgical plans during procedures, which hinders attaining higher levels of robotic autonomy. These systems adhere to conventional rigid registration concepts, relying on the alignment of preoperative planning to the intraoperative anatomy. In this paper, we propose a safe deep reinforcement learning (DRL) planning approach (SafeRPlan) for robotic spine surgery that leverages intraoperative observation for continuous path planning of pedicle screw placement. The main contributions of our method are (1) the capability to ensure safe actions by introducing an uncertainty-aware distance-based safety filter; (2) the ability to compensate for incomplete intraoperative anatomical information, by encoding a-priori knowledge of anatomical structures with neural networks pre-trained on pre-operative images; and (3) the capability to generalize over unseen observation noise thanks to the novel domain randomization techniques. Planning quality was assessed by quantitative comparison with the baseline approaches, gold standard (GS) and qualitative evaluation by expert surgeons. In experiments with human model datasets, our approach was capable of achieving over 5% higher safety rates compared to baseline approaches, even under realistic observation noise. To the best of our knowledge, SafeRPlan is the first safety-aware DRL planning approach specifically designed for robotic spine surgery.

中文翻译：

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SafeRPlan：用于椎弓根螺钉放置术中规划的安全深度强化学习


脊柱融合手术需要高度准确地植入椎弓根螺钉植入物，这必须在靠近重要结构且解剖结构视野有限的情况下进行。已经提出了机器人手术系统来提高放置精度。尽管取得了显着进步，但当前的机器人系统仍然缺乏在手术过程中不断更新手术计划的先进机制，这阻碍了实现更高水平的机器人自主性。这些系统遵循传统的严格套准概念，依赖于术前计划与术中解剖结构的一致性。在本文中，我们提出了一种用于机器人脊柱手术的安全深度强化学习 （DRL） 规划方法 （SafeRPlan），该方法利用术中观察进行椎弓根螺钉放置的连续路径规划。我们方法的主要贡献是 （1） 通过引入不确定性感知基于距离的安全滤波器来确保安全行动的能力;（2） 通过使用在术前图像上预先训练的神经网络编码解剖结构的先验知识，补偿不完整的术中解剖信息的能力;（3） 由于新颖的域随机化技术，能够泛化看不见的观察噪声。通过与基线方法、金标准 （GS） 的定量比较和专家外科医生的定性评估来评估计划质量。在人类模型数据集的实验中，即使在真实的观察噪声下，我们的方法也能够实现比基线方法高 5% 以上的安全率。据我们所知，SafeRPlan 是第一个专为机器人脊柱手术设计的安全意识 DRL 计划方法。