Reinforcement learning for safe autonomous two-device navigation of cerebral vessels in mechanical thrombectomy.

Journal: International journal of computer assisted radiology and surgery
Published Date: Apr 3, 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.

Authors

  • Harry Robertshaw
    Biomedical Engineering and Imaging Sciences, Kings College London, 1 Lambeth Palace Rd, London, SE1 7EU, UK.
  • Benjamin Jackson
    Biomedical Engineering and Imaging Sciences, Kings College London, 1 Lambeth Palace Rd, London, SE1 7EU, UK.
  • Jiaheng Wang
    School of Computer Science, Zhejiang Universty, Hangzhou 310000, P.R.China.
  • Hadi Sadati
    Surgical and Interventional Engineering, School of Biomedical Engineering and Imaging Sciences, Kings College London, London, UK.
  • Lennart Karstensen
    Fraunhofer IPA, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany. Lennart.karstensen@ipa.fraunhofer.de.
  • Alejandro Granados
    Surgical and Interventional Engineering, King's College London, London, UK. alejandro.granados@kcl.ac.uk.
  • Thomas C Booth
    School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, United Kingdom.

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