Semantic segmentation deep learning model boosts surgeons’ organ recognition in minimally invasive hysterectomy – a prospective multi-center reader performance study using pre-selected video clips
The AI-based system enhanced surgeons' ability to recognize the ureter and bladder on surgical video, particularly among less experienced surgeons, without increasing the risk of misidentification. AI may help improve intraoperative anatomical identification and potentially contribute to safer, more standardized surgical performance.
Authors
- Yuki Koike (ORCID: https://orcid.org/0000-0002-9325-256X)
- Junki Onishi (ORCID: https://orcid.org/0009-0002-4473-6485)
- Nobuyoshi TAKESHITA
- Mitsumasa Homma
- K. Matsumoto
- Yusuke Hirose (ORCID: https://orcid.org/0000-0001-8278-4918)
- Suguru Odajima (ORCID: https://orcid.org/0000-0003-1000-1974)
- Shin Takenaka (ORCID: https://orcid.org/0000-0002-4333-6271)
- Hiroki Matsuzaki
- Ryo Koike
- Makoto Nakabayashi
- Yuichi Harada
- Daichi Kitaguchi
- Hiroshi Tanabe
Institutions
- SHOWA Medical University (JP)
- Chiba University (JP)
- National Cancer Center Hospital East (JP)
- SHOWA Medical University Hospital (JP)
Publication Details
- Journal
- International Journal of Surgery
- Published
- 2025-12-22
- DOI
- https://doi.org/10.1097/js9.0000000000004606
- Primary Topic
- Surgical Simulation and Training
- Type
- article
- Field-Weighted Citation Impact
- 0.00