Imitation Learning for Path Planning in Cardiac Percutaneous Interventions
Embedding task demonstrations in the learning process shows the potential to automate and optimize catheter navigation, promoting standardization of minimally invasive cardiac procedures.
Authors
- Angela Peloso (ORCID: https://orcid.org/0000-0002-6395-8369)
- Emiliano Votta (ORCID: https://orcid.org/0000-0001-7115-0151)
- Anna Bicchi (ORCID: https://orcid.org/0000-0003-0751-2669)
- Damiano Rizzoni (ORCID: https://orcid.org/0000-0002-1253-0186)
- Elena De Momi (ORCID: https://orcid.org/0000-0002-8819-2734)
- Xiu Zhang (ORCID: https://orcid.org/0000-0001-8721-4316)
Institutions
- National Academies of Sciences, Engineering, and Medicine (US)
- Construction Technologies Institute (IT)
- Institute of Biomedical Technologies (IT)
- National Research Council (IT)
- Politecnico di Milano (IT)
Publication Details
- Journal
- IEEE Transactions on Biomedical Engineering
- Published
- 2025-02-13
- DOI
- https://doi.org/10.1109/tbme.2025.3542224
- Citations
- 4
- Primary Topic
- Soft Robotics and Applications
- Type
- article
- Field-Weighted Citation Impact
- 2.70