Operative Time Prediction by Machine Learning for Robot‐Assisted Laparoscopic Radical Prostatectomy
The system provides a reliable and robust prediction focusing on factors known to impact operative time. It has the potential to improve the efficiency of operating room scheduling.
Authors
- Akihiro Ito (ORCID: https://orcid.org/0000-0001-7224-1100)
- Tomoya Kobayashi (ORCID: https://orcid.org/0000-0001-8179-1702)
- Shinya Sonobe (ORCID: https://orcid.org/0000-0002-3998-4612)
- Naoki Kawamorita (ORCID: https://orcid.org/0000-0001-6175-3063)
- Saori Ikumi
- Yu Suzuki (ORCID: https://orcid.org/0000-0003-4085-6535)
- Junya Iwazaki
- Yoshihide Kawasaki (ORCID: https://orcid.org/0000-0001-6425-3574)
- Eichi Takaya (ORCID: https://orcid.org/0000-0003-2541-1685)
- Hisanobu Adachi
- Carlos Makoto Miyauchi
Institutions
- Tohoku University (JP)
- Tohoku University Hospital (JP)
- Miyagi Prefectural Hospital Organization (JP)
Publication Details
- Journal
- International Journal of Urology
- Published
- 2025-11-30
- DOI
- https://doi.org/10.1111/iju.70292
- Primary Topic
- Surgical Simulation and Training
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministry of Education, Culture, Sports, Science and Technology