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This paper explores the use of world models, specifically TD-MPC2, for autonomous endovascular navigation in mechanical thrombectomy, addressing the limitations of RL in handling diverse patient anatomies. TD-MPC2, which integrates planning and learned dynamics, was trained on multiple navigation tasks and benchmarked against SAC, showing superior success rates in simulation (58% vs. 36%) and comparable in vitro performance. The study provides the first demonstration of autonomous MT navigation validated across both in silico and in vitro experiments, suggesting the potential of world models for safe and generalizable AI-assisted interventions.
World models can navigate blood vessels autonomously with higher success rates than standard RL, paving the way for safer robotic stroke treatments.
Autonomous mechanical thrombectomy (MT) presents substantial challenges due to highly variable vascular geometries and the requirements for accurate, real-time control. While reinforcement learning (RL) has emerged as a promising paradigm for the automation of endovascular navigation, existing approaches often show limited robustness when faced with diverse patient anatomies or extended navigation horizons. In this work, we investigate a world-model-based framework for autonomous endovascular navigation built on TD-MPC2, a model-based RL method that integrates planning and learned dynamics. We evaluate a TD-MPC2 agent trained on multiple navigation tasks across hold out patient-specific vasculatures and benchmark its performance against the state-of-the-art Soft Actor-Critic (SAC) algorithm agent. Both approaches are further validated in vitro using patient-specific vascular phantoms under fluoroscopic guidance. In simulation, TD-MPC2 demonstrates a significantly higher mean success rate than SAC (58% vs. 36%, p<0.001), and mean tip contact forces of 0.15 N, well below the proposed 1.5 N vessel rupture threshold. Mean success rates for TD-MPC2 (68%) were comparable to SAC (60%) in vitro, but TD-MPC2 achieved superior path ratios (p = 0.017) at the cost of longer procedure times (p<0.001). Together, these results provide the first demonstration of autonomous MT navigation validated across both hold out in silico data and fluoroscopy-guided in vitro experiments, highlighting the promise of world models for safe and generalizable AI-assisted endovascular interventions.
