机械取栓术中脑血管安全自主双器械导航的强化学习
Reinforcement Learning for Safe Autonomous Two Device Navigation of Cerebral Vessels in Mechanical Thrombectomy
AI总结：
提出一种安全的双器械强化学习算法，通过整合导丝尖端力保障患者安全，首次实现机械取栓术中微导管与微导丝向脑血管的自主导航，在未见病例中取得96%成功率且作用力远低于破裂阈值。
摘要：
目的：机械取栓术（MT）中的自主系统在减少手术时间、最小化辐射暴露和增强患者安全性方面具有广阔前景。然而，当前的强化学习（RL）方法仅能到达颈动脉，无法泛化到其他患者血管结构，且未考虑安全性。我们提出了一种安全的双器械RL算法，能够导航至颈动脉以外的脑血管。方法：我们使用Simulation Open Framework Architecture来表征脑血管的复杂性，并使用改进的Soft Actor-Critic RL算法首次学习了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中。在12个患者特定的血管病例上使用演示者数据进行逆向RL。结果：我们的模拟在未见过的脑血管中实现了成功的自主导航，达到96%的成功率、7.0秒的手术时间和0.24 N的平均力，远低于提出的1.5 N血管破裂阈值。结论：据我们所知，我们提出的用于MT双器械导航的自主系统首次到达脑血管，考虑了安全性，并可泛化到未见过的患者特定病例。我们设想未来的工作将把验证扩展到不同复杂度的血管和体外模型。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要素。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
