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

2026-02-20

AI Summary

This work proposes a hierarchical modular multi-agent reinforcement learning (HM-MARL) framework to address the limited interventional accessibility in mechanical thrombectomy due to geographical and logistical constraints, as well as the poor generalization of existing reinforcement learning methods in long-horizon vascular navigation. The approach decomposes the complex task of navigating a catheter–guidewire system from the femoral artery to the internal carotid artery into multiple subtasks, each handled by a dedicated agent trained independently using the Soft Actor-Critic algorithm. Autonomous navigation is demonstrated for the first time in an in vitro thrombectomy vascular environment, achieving digital success rates of 92–100% on single anatomies and 56–80% across multiple anatomies. In physical experiments, the system reached the right common carotid artery in 100% of trials and the right internal carotid artery in 80%, significantly enhancing cross-patient anatomical generalization and clinical feasibility.

Technology Category

• Multiagent Systems: Adversarial Agents
• Intelligent Robots: Multi-Robot Systems
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Mechanical Thrombectomy
• Autonomous Navigation
• Reinforcement Learning
• Endovascular Intervention
• Generalization

Innovation

• Hierarchical Modular Multi-Agent Reinforcement Learning
• Autonomous Navigation
• Mechanical Thrombectomy
• Soft Actor-Critic
• Simulation-to-Real Transfer