Robertshaw Harry, Jackson Benjamin, Wang Jiaheng, Sadati Hadi, Karstensen Lennart, Granados Alejandro, Booth Thomas C
Surgical and Interventional Engineering, School of Biomedical Engineering and Imaging Sciences, Kings College London, London, UK.
AIBE, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.
Int J Comput Assist Radiol Surg. 2025 Apr 3. doi: 10.1007/s11548-025-03339-8.
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.
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.
Our simulation demonstrates successful autonomous navigation within unseen cerebral vessels, achieving a 96% success rate, 7.0 s procedure time, and 0.24 N mean forces, well below the proposed 1.5 N vessel rupture threshold.
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 toward deploying agents in clinical settings, safety and trustworthiness will be crucial elements to consider when proposing new methodology.
机械取栓术（MT）中的自主系统有望缩短手术时间、减少辐射暴露并提高患者安全性。然而，当前的强化学习（RL）方法仅能到达颈动脉，无法推广到其他患者的血管系统，且未考虑安全性。我们提出一种安全的双设备RL算法，该算法能够超越颈动脉，导航至脑血管。
我们使用仿真开放框架架构来表示脑血管的复杂性，并使用一种改进的软 Actor-Critic RL算法首次学习微导管和微导丝的导航。我们通过整合导丝尖端力将患者安全指标纳入奖励函数。逆强化学习与12个特定患者血管病例的示范数据一起使用。
我们的模拟展示了在未见的脑血管内成功进行自主导航，成功率达到96%，手术时间为7.0秒，平均力为0.24牛，远低于提议的1.5牛血管破裂阈值。
据我们所知，我们提出的用于MT双设备导航的自主系统首次能够到达脑血管、考虑安全性并且可推广到未见的特定患者病例。我们设想未来的工作将把验证扩展到不同复杂程度的血管系统以及体外模型。虽然我们的贡献为在临床环境中部署智能体铺平了道路，但在提出新方法时，安全性和可信度将是需要考虑的关键因素。
