# World Model for AI Autonomous Navigation in Mechanical Thrombectomy

arXiv: https://arxiv.org/abs/2509.25518 · HTML: https://mlsanity.com/paper/2509.25518/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Medical and surgical world models, one of 11 directions in World models.
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Placed in

- World models / [Medical and surgical world models](https://mlsanity.com/wm/medical-and-surgical-world-models/)
- Deep reinforcement learning methods / [RL for clinical treatment and surgery](https://mlsanity.com/deeprl/rl-for-clinical-treatment-and-surgery/)

## Builds on (most cited first, 10 of 10)

- [Mastering Diverse Domains through World Models](https://mlsanity.com/paper/2301.04104/) (2023)
- [Open X-Embodiment: Robotic Learning Datasets and RT-X Models](https://mlsanity.com/paper/2310.08864/) (2023)
- [Recurrent World Models Facilitate Policy Evolution](https://mlsanity.com/paper/1809.01999/) (2018)
- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [GAIA-1: A Generative World Model for Autonomous Driving](https://mlsanity.com/paper/2309.17080/) (2023)
- [TD-MPC2: Scalable, Robust World Models for Continuous Control](https://mlsanity.com/paper/2310.16828/) (2023)
- [CathSim: An Open-source Simulator for Endovascular Intervention](https://mlsanity.com/paper/2208.01455/) (2022)
- [Artificial Intelligence in the Autonomous Navigation of Endovascular Interventions: A Systematic Review](https://mlsanity.com/paper/2405.03305/) (2024)
- [Learning-Based Autonomous Navigation, Benchmark Environments and Simulation Framework for Endovascular Interventions](https://mlsanity.com/paper/2410.01956/) (2024)
- [Reinforcement Learning for Safe Autonomous Two Device Navigation of Cerebral Vessels in Mechanical Thrombectomy](https://mlsanity.com/paper/2503.24140/) (2025)

## Built on by (most cited first, 5 of 5)

- [MRI Contrast Enhancement Kinetics World Model](https://mlsanity.com/paper/2602.19285/) (2026)
- [Medical world models: representing medical states, modelling clinical dynamics and guiding intervention policies](https://mlsanity.com/paper/2606.16721/) (2026)
- [Toward AI Autonomous Navigation for Mechanical Thrombectomy using Hierarchical Modular Multi-agent Reinforcement Learning (HM-MARL)](https://mlsanity.com/paper/2602.18663/) (2026)
- [ACCURATE: Arbitrary-shaped Continuum Reconstruction Under Robust Adaptive Two-view Estimation](https://mlsanity.com/paper/2603.07533/) (2026)
- [Autonomous Robotic Navigation for Endovascular Brain-Computer Interface Access](https://mlsanity.com/paper/2610.03537/) (2026)

## Abstract (arXiv)

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.