Toward AI Autonomous Navigation for Mechanical Thrombectomy using Hierarchical Modular Multi-agent Reinforcement Learning (HM-MARL)

TL;DR

This study presents the first demonstration of in vitro autonomous navigation in MT vasculature, and proposes a Hierarchical Modular Multi-Agent Reinforcement Learning framework for autonomous two-device navigation in vitro, enabling efficient and generalizable navigation.

Abstract

Mechanical thrombectomy (MT) is typically the optimal treatment for acute ischemic stroke involving large vessel occlusions, but access is limited due to geographic and logistical barriers. Reinforcement learning (RL) shows promise in autonomous endovascular navigation, but generalization across 'long' navigation tasks remains challenging. We propose a Hierarchical Modular Multi-Agent Reinforcement Learning (HM-MARL) framework for autonomous two-device navigation in vitro, enabling efficient and generalizable navigation. HM-MARL was developed to autonomously navigate a guide catheter and guidewire from the femoral artery to the internal carotid artery (ICA). A modular multi-agent approach was used to decompose the complex navigation task into specialized subtasks, each trained using Soft Actor-Critic RL. The framework was validated in both in silico and in vitro testbeds to assess generalization and real-world feasibility. In silico, a single-vasculature model achieved 92-100% success rates on individual anatomies, while a multi-vasculature model achieved 56-80% across multiple patient anatomies. In vitro, both HM-MARL models successfully navigated 100% of trials from the femoral artery to the right common carotid artery and 80% to the right ICA but failed on the left-side vessel superhuman challenge due to the anatomy and catheter type used in navigation. This study presents the first demonstration of in vitro autonomous navigation in MT vasculature. While HM-MARL enables generalization across anatomies, the simulation-to-real transition introduces challenges. Future work will refine RL strategies using world models and validate performance on unseen in vitro data, advancing autonomous MT towards clinical translation.

Figures (6)

• Figure 1: Full patient MT vasculature with sub-tasks labeled for the first phase of MT (navigating a guide wire and catheter from the common iliac artery the left or right ICA). Labels based on radiological left and right, where left sided anatomy is harder to navigate due to more acute angle of left common carotid artery (CCA) compared to the right. $A_1$ begins at the insertion point, hence no start area is labeled.
• Figure 2: in vitro testbed showing the 3D phantom and tracking camera. Inset is a side view of the 3D phantom.
• Figure 3: Concentric tube robot (CTR) manipulator used for in vitro experiments Sadati2025.
• Figure 4: HM-MARL. TSM is used to select $a_{t+1}$ based on $s_t$.
• Figure 5: in vitro navigation paths for $A_{1,2}$ and $A_{2,3}$. Successful trajectories are for right-sided navigation tasks, while unsuccessful trajectories are for left-sided navigation tasks.