用世界模型提升血管内手术机器人自主导航能力
World Model for AI Autonomous Navigation in Mechanical Thrombectomy
- 基于模型的强化学习构建血管导航世界模型
- 多患者数据训练下成功率提升至65%（SAC为37%）
- 适合需高精度导航的智能医疗机器人研究者
机械血栓切除术（MT）的自主导航因血管解剖结构复杂且需实时精准决策，仍是重大挑战。基于强化学习（RL）的方法虽具潜力，但现有方法在跨患者血管和长时程任务中泛化能力不足。本文提出采用基于模型的强化学习算法TD-MPC2，构建用于自主血管内导航的世界模型。我们在10例真实患者血管中，训练单一RL代理完成多项导航任务，并与当前最优的Soft Actor-Critic（SAC）方法对比。结果表明，TD-MPC2在多任务学习中显著优于SAC，平均成功率提升至65%（SAC为37%），路径效率也有明显改善。尽管执行时间增加，显示出成功率与速度的权衡，但验证了世界模型在提升自主血管导航能力方面的潜力，为可泛化的智能机器人介入治疗研究奠定基础。
原文摘要 · Abstract (English)
Autonomous navigation for mechanical thrombectomy (MT) remains a critical challenge due to the complexity of vascular anatomy and the need for precise, real-time decision-making. Reinforcement learning (RL)-based approaches have demonstrated potential in automating endovascular navigation, but current methods often struggle with generalization across multiple patient vasculatures and long-horizon tasks. We propose a world model for autonomous endovascular navigation using TD-MPC2, a model-based RL algorithm. We trained a single RL agent across multiple endovascular navigation tasks in ten real patient vasculatures, comparing performance against the state-of-the-art Soft Actor-Critic (SAC) method. Results indicate that TD-MPC2 significantly outperforms SAC in multi-task learning, achieving a 65% mean success rate compared to SAC's 37%, with notable improvements in path ratio. TD-MPC2 exhibited increased procedure times, suggesting a trade-off between success rate and execution speed. These findings highlight the potential of world models for improving autonomous endovascular navigation and lay the foundation for future research in generalizable AI-driven robotic interventions.
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