The AENEAS Project: Intraoperative Anatomical Guidance Through Real-Time Landmark Detection Using Machine Vision
Applying machine vision techniques for anatomical detection in a complex neurosurgical setting is feasible. Although challenges remain in detecting less distinctive structures, the high accuracy achieved for deep anatomical landmarks validates this approach. This study marks an essential step toward the development of machine vision-powered anatomical recognition tools, with the prospective goal of improving intraoperative orientation and reducing variability among surgeons.
Authors
- Victor E. Staartjes (ORCID: https://orcid.org/0000-0003-1039-2098)
- Ender Konukoğlu (ORCID: https://orcid.org/0000-0002-2542-3611)
- Simone Olei (ORCID: https://orcid.org/0000-0002-2558-3017)
- Carlo Serra (ORCID: https://orcid.org/0000-0002-7305-550X)
- Luca Regli (ORCID: https://orcid.org/0000-0003-4639-4474)
- Seungjun Ryu (ORCID: https://orcid.org/0000-0001-5547-9811)
- Gary Sarwin (ORCID: https://orcid.org/0000-0002-9011-4882)
- Luca Zanuttini (ORCID: https://orcid.org/0009-0007-6195-1706)
Institutions
- ETH Zurich (CH)
- Ente Ospedaliero Cantonale (CH)
- University Hospital of Zurich (CH)
- Daejeon Eulji Medical Center, Eulji University (KR)
- University of Bologna (IT)
Publication Details
- Journal
- Mayo Clinic Proceedings Digital Health
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1016/j.mcpdig.2025.100308
- Primary Topic
- Soft Robotics and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung