World Model for AI Autonomous Navigation in Mechanical Thrombectomy

• URL: http://arxiv.org/abs/2509.25518v2
• Date: Thu, 02 Oct 2025 05:02:54 GMT
• Title: World Model for AI Autonomous Navigation in Mechanical Thrombectomy
• Authors: Harry Robertshaw, Han-Ru Wu, Alejandro Granados, Thomas C Booth,
• Abstract summary: We propose a world model for autonomous endovascular navigation using TD-MPC2, a model-based RL algorithm.<n>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.<n>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.
• Score: 39.51808126417126
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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