Reinforcement Learning for Safe Autonomous Two Device Navigation of Cerebral Vessels in Mechanical Thrombectomy
Journal:
arXiv
Published Date:
Mar 31, 2025
Abstract
Purpose: Autonomous systems in mechanical thrombectomy (MT) hold promise for
reducing procedure times, minimizing radiation exposure, and enhancing patient
safety. However, current reinforcement learning (RL) methods only reach the
carotid arteries, are not generalizable to other patient vasculatures, and do
not consider safety. We propose a safe dual-device RL algorithm that can
navigate beyond the carotid arteries to cerebral vessels.
Methods: We used the Simulation Open Framework Architecture to represent the
intricacies of cerebral vessels, and a modified Soft Actor-Critic RL algorithm
to learn, for the first time, the navigation of micro-catheters and
micro-guidewires. We incorporate patient safety metrics into our reward
function by integrating guidewire tip forces. Inverse RL is used with
demonstrator data on 12 patient-specific vascular cases.
Results: Our simulation demonstrates successful autonomous navigation within
unseen cerebral vessels, achieving a 96% success rate, 7.0s procedure time, and
0.24 N mean forces, well below the proposed 1.5 N vessel rupture threshold.
Conclusion: To the best of our knowledge, our proposed autonomous system for
MT two-device navigation reaches cerebral vessels, considers safety, and is
generalizable to unseen patient-specific cases for the first time. We envisage
future work will extend the validation to vasculatures of different complexity
and on in vitro models. While our contributions pave the way towards deploying
agents in clinical settings, safety and trustworthiness will be crucial
elements to consider when proposing new methodology.
