Development of an Artificial Intelligence for Detecting False Positive Detection to Manage the Accuracy of Artificial Intelligence in Colonoscopy
Objectives: False positive detection (FPD) is a clinically relevant challenge that must be solved to ensure the safe and effective integration of artificial intelligence (AI) into colonoscopy. This study aimed to develop a novel AI system for quantifying FPD and to compare FPD rates between two commercially available AI platforms.
Authors
- Reo Kobayashi (ORCID: https://orcid.org/0000-0002-0910-6915)
- Hardesh Dhillon (ORCID: https://orcid.org/0000-0003-2258-0950)
- N. Iwai (ORCID: https://orcid.org/0000-0002-6943-1605)
- Tomohisa Takagi (ORCID: https://orcid.org/0000-0002-6119-4828)
- Naohisa Yoshida (ORCID: https://orcid.org/0000-0001-6167-9705)
- Ruiyao Zhang (ORCID: https://orcid.org/0009-0003-8760-3452)
- Xin Zhu (ORCID: https://orcid.org/0000-0002-4376-0806)
- Yasutaka Morimoto
- T Yasuda
- O Dohi
- Ryohei Hirose
- Ken Inoue
Institutions
- Barwon Health (AU)
- University of Aizu (JP)
- Kyoto Prefectural University of Medicine (JP)
- Innovative Research (United States) (US)
- Fukui-ken Saiseikai Hospital (JP)
Publication Details
- Journal
- Journal of the Anus Rectum and Colon
- Published
- 2026-07-24
- DOI
- https://doi.org/10.23922/jarc.2025-112
- Primary Topic
- Colorectal Cancer Screening and Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00