Purpose: Autonomous systems in mechanical thrombectomy (MT) hold promise for reducing procedure times, minimizing radiation exposure, and enhancing patient safety. However, current reinforcement learning (RL) methods only reach the carotid arteries, are not generalizable to other patient vasculatures, and do not consider safety. We propose a safe dual-device RL algorithm that can navigate beyond the carotid arteries to cerebral vessels. Methods: We used the Simulation Open Framework Architecture to represent the intricacies of cerebral vessels, and a modified Soft Actor-Critic RL algorithm to learn, for the first time, the navigation of micro-catheters and micro-guidewires. We incorporate patient safety metrics into our reward function by integrating guidewire tip forces. Inverse RL is used with demonstrator data on 12 patient-specific vascular cases. Results: Our simulation demonstrates successful autonomous navigation within unseen cerebral vessels, achieving a 96% success rate, 7.0s procedure time, and 0.24 N mean forces, well below the proposed 1.5 N vessel rupture threshold. Conclusion: To the best of our knowledge, our proposed autonomous system for MT two-device navigation reaches cerebral vessels, considers safety, and is generalizable to unseen patient-specific cases for the first time. We envisage future work will extend the validation to vasculatures of different complexity and on in vitro models. While our contributions pave the way towards deploying agents in clinical settings, safety and trustworthiness will be crucial elements to consider when proposing new methodology.

翻译：目的：机械取栓（MT）中的自主系统有望缩短手术时间、减少辐射暴露并提升患者安全性。然而，当前的强化学习（RL）方法仅能到达颈动脉，无法泛化至其他患者血管结构，且未考虑安全性。我们提出一种安全的双器械强化学习算法，能够超越颈动脉区域实现脑血管导航。方法：我们采用仿真开放框架架构（Simulation Open Framework Architecture）模拟脑血管的复杂结构，并利用改进的柔性演员-评论家（Soft Actor-Critic）强化学习算法，首次实现了微导管与微导丝协同导航的学习。通过将导丝尖端受力信息整合至奖励函数，我们将患者安全指标纳入算法框架。基于12例患者特异性血管病例的示范数据，采用逆向强化学习进行训练。结果：仿真实验表明，该系统在未见过的脑血管中实现了成功自主导航，达成96%的成功率、7.0秒的操作时间及0.24N的平均受力，远低于提出的1.5N血管破裂阈值。结论：据我们所知，本研究提出的机械取栓双器械自主导航系统首次实现了脑血管区域的抵达，兼顾安全性，并能泛化至未见过的患者特异性病例。我们展望未来工作将扩展验证至不同复杂度的血管结构及体外模型。尽管本研究为临床环境部署智能体奠定了基础，但在提出新方法时，安全性与可信度仍是需要考量的核心要素。