Benchmarking reinforcement learning algorithms for autonomous mechanical thrombectomy
This work advances autonomous endovascular navigation by establishing a benchmark for MT. The results emphasize the importance of hyperparameter tuning on the performance of RL algorithms. Future research should extend this benchmark to identify the most effective RL algorithm.
Authors
- Thomas C. Booth (ORCID: https://orcid.org/0000-0003-0984-3998)
- Lennart Karstensen
- Harry Robertshaw (ORCID: https://orcid.org/0000-0002-5486-8062)
- Alejandro Granados (ORCID: https://orcid.org/0000-0002-2866-1324)
- Farhana Moosa
Institutions
- King's College - North Carolina (US)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- King's College London (GB)
- King's College Hospital (GB)
Publication Details
- Journal
- International Journal of Computer Assisted Radiology and Surgery
- Published
- 2025-04-29
- DOI
- https://doi.org/10.1007/s11548-025-03360-x
- Citations
- 5
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 6.77
Funders
- Wellcome Trust
- Engineering and Physical Sciences Research Council