Benchmarking reinforcement learning algorithms for autonomous mechanical thrombectomy

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Farhana Moosa, Harry Robertshaw, Lennart Karstensen, Thomas C. Booth, Alejandro Granados

Published: 29 Apr 2025, Last Modified: 15 Apr 2026International Journal of Computer Assisted Radiology and SurgeryEveryoneRevisionsCC BY-SA 4.0
Abstract: Mechanical thrombectomy (MT) is the gold standard for treating acute ischemic stroke. However, challenges such as operator radiation exposure, reliance on operator experience, and limited treatment access remain. Although autonomous robotics could mitigate some of these limitations, current research lacks benchmarking of reinforcement learning (RL) algorithms for MT. This study aims to evaluate the performance of Deep Deterministic Policy Gradient, Twin Delayed Deep Deterministic Policy Gradient, Soft Actor-Critic, and Proximal Policy Optimization for MT.