Autonomous countertraction for secure field of view in laparoscopic surgery using deep reinforcement learning
The proposed neural network was validated in both simulation and phantom experimental settings, confirming that traction control improved tissue planarity and visibility. These results demonstrate the feasibility of automating countertraction using the proposed model.
Authors
- Yudai Takahashi
- Naoki Tomii (ORCID: https://orcid.org/0000-0002-5485-4883)
- Takumi Noda (ORCID: https://orcid.org/0000-0002-3741-0017)
- Etsuko Kobayashi (ORCID: https://orcid.org/0000-0003-1794-0173)
- Kazuaki Hara (ORCID: https://orcid.org/0000-0001-7291-6825)
- Ichiro Sakuma (ORCID: https://orcid.org/0000-0002-4919-9466)
- Jiahe Chen (ORCID: https://orcid.org/0000-0001-7584-7861)
- Yuriko Iyama
Institutions
- The University of Tokyo (JP)
Publication Details
- Journal
- International Journal of Computer Assisted Radiology and Surgery
- Published
- 2024-09-16
- DOI
- https://doi.org/10.1007/s11548-024-03264-2
- Citations
- 3
- Primary Topic
- Surgical Simulation and Training
- Type
- article
- Field-Weighted Citation Impact
- 1.72
Funders
- University of Tokyo
- Moonshot Research and Development Program