External Validation of an Automated Surgical Step Recognition Model for Robotic Distal Gastrectomy ( <scp>RDG</scp> ) Using a Multicenter Dataset
We demonstrated that an AI step recognition model for RDG can predict surgical steps in an external video dataset with moderate accuracy. Furthermore, we conclude that training an AI model on a multi-institutional dataset significantly increases its step recognition capabilities. These results confirm that our model can be utilized by external institutions, and that a diverse training set of RDG procedures from multiple institutions is valuable to developing an AI model with precise step recognition capabilities in new institutions.
Authors
- James S. Strong
- Takahiro Kinoshita (ORCID: https://orcid.org/0000-0001-7365-8733)
- Masashi Takeuchi (ORCID: https://orcid.org/0000-0003-3797-432X)
- Masahiro Yura (ORCID: https://orcid.org/0000-0001-7102-9212)
- Satoru Matsuda (ORCID: https://orcid.org/0000-0001-7538-6962)
- Yusuke Maeda
- Tasuku Furube
- Hirofumi Kawakubo (ORCID: https://orcid.org/0000-0002-6246-7023)
- Yuko Kitagawa (ORCID: https://orcid.org/0000-0002-0271-5670)
Institutions
- Keio University (JP)
- Harvard University Press (US)
- National Cancer Center Hospital East (JP)
Publication Details
- Journal
- Annals of Gastroenterological Surgery
- Published
- 2025-05-20
- DOI
- https://doi.org/10.1002/ags3.70042
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Keio University