Impact of a machine learning–based prediction model on annual surveillance endoscopy costs for detecting gastric cancer
Our ML model holds promise in reducing endoscopy surveillance costs while maintaining a robust GC detection rate.
Authors
- Ryota Niikura (ORCID: https://orcid.org/0000-0001-5047-6195)
- Tetsuo Ushiku (ORCID: https://orcid.org/0000-0002-1763-8380)
- Atsushi Miyawaki (ORCID: https://orcid.org/0000-0001-6126-1464)
- Junya Arai (ORCID: https://orcid.org/0000-0002-4905-7046)
- Tomonori Aoki (ORCID: https://orcid.org/0000-0003-0794-9940)
- Mitsuhiro Fujishiro (ORCID: https://orcid.org/0000-0002-4074-1140)
- Yoku Hayakawa (ORCID: https://orcid.org/0000-0002-3988-2499)
- Hiroaki Fujiwara (ORCID: https://orcid.org/0000-0003-0519-2796)
- Masato Kasuga
Institutions
- The Institute of Medical Science, Asahi Life Foundation (JP)
- University of Tokyo Health Sciences (JP)
- The University of Tokyo (JP)
Publication Details
- Journal
- iGIE
- Published
- 2024-09-06
- DOI
- https://doi.org/10.1016/j.igie.2024.09.003
- Citations
- 1
- Primary Topic
- Gastric Cancer Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.42
Funders
- Japan Agency for Medical Research and Development
- Japan Society for the Promotion of Science