# Autonomous navigation of catheters and guidewires in mechanical thrombectomy using inverse reinforcement learning

arXiv: https://arxiv.org/abs/2406.12499 · HTML: https://mlsanity.com/paper/2406.12499/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in RL for clinical treatment and surgery, one of 15 directions in Deep reinforcement learning methods.
- Builds on 3 of its 40 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Placed in

- 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, 3 of 3)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Deep reinforcement learning for guidewire navigation in coronary artery phantom](https://mlsanity.com/paper/2110.01840/) (2021)
- [Artificial Intelligence in the Autonomous Navigation of Endovascular Interventions: A Systematic Review](https://mlsanity.com/paper/2405.03305/) (2024)

## Built on by (most cited first, 7 of 7)

- [Reinforcement Learning for Safe Autonomous Two Device Navigation of Cerebral Vessels in Mechanical Thrombectomy](https://mlsanity.com/paper/2503.24140/) (2025)
- [Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutions](https://mlsanity.com/paper/2504.15327/) (2025)
- [Towards Next-Generation Healthcare: A Survey of Medical Embodied AI for Perception, Decision-Making, and Action](https://mlsanity.com/paper/2606.15647/) (2026)
- [DARIL: When Imitation Learning outperforms Reinforcement Learning in Surgical Action Planning](https://mlsanity.com/paper/2507.05011/) (2025)
- [Toward AI Autonomous Navigation for Mechanical Thrombectomy using Hierarchical Modular Multi-agent Reinforcement Learning (HM-MARL)](https://mlsanity.com/paper/2602.18663/) (2026)
- [Towards Real-Time Autonomous Navigation: Transformer-Based Catheter Tip Tracking in Fluoroscopy](https://mlsanity.com/paper/2605.14253/) (2026)
- [DINO-CVA: A Multimodal Goal-Conditioned Vision-to-Action Model for Autonomous Catheter Navigation](https://mlsanity.com/paper/2510.17038/) (2025)

## Abstract (arXiv)

Purpose: Autonomous navigation of catheters and guidewires can enhance endovascular surgery safety and efficacy, reducing procedure times and operator radiation exposure. Integrating tele-operated robotics could widen access to time-sensitive emergency procedures like mechanical thrombectomy (MT). Reinforcement learning (RL) shows potential in endovascular navigation, yet its application encounters challenges without a reward signal. This study explores the viability of autonomous navigation in MT vasculature using inverse RL (IRL) to leverage expert demonstrations. Methods: This study established a simulation-based training and evaluation environment for MT navigation. We used IRL to infer reward functions from expert behaviour when navigating a guidewire and catheter. We utilized soft actor-critic to train models with various reward functions and compared their performance in silico. Results: We demonstrated feasibility of navigation using IRL. When evaluating single versus dual device (i.e. guidewire versus catheter and guidewire) tracking, both methods achieved high success rates of 95% and 96%, respectively. Dual-tracking, however, utilized both devices mimicking an expert. A success rate of 100% and procedure time of 22.6 s were obtained when training with a reward function obtained through reward shaping. This outperformed a dense reward function (96%, 24.9 s) and an IRL-derived reward function (48%, 59.2 s). Conclusions: We have contributed to the advancement of autonomous endovascular intervention navigation, particularly MT, by employing IRL. The results underscore the potential of using reward shaping to train models, offering a promising avenue for enhancing the accessibility and precision of MT. We envisage that future research can extend our methodology to diverse anatomical structures to enhance generalizability.