Artificial Intelligence-Based Classification of Anatomical Sites in Esophagogastroduodenoscopy Images
The AIMED system achieved high accuracy with regard to recognizing gastric anatomy sites, and it could assist the operator in enhancing the quality control of the used endoscope. Moreover, it could contribute to a more standardized endoscopic performance. Overall, our findings prove that artificial-intelligence-based systems can be indispensable to the endoscopic revolution (Clinical trial registration number: NCT04384575 (12/05/2020)).
Authors
- Qi Wu (ORCID: https://orcid.org/0000-0001-6182-5274)
- Jing Wang (ORCID: https://orcid.org/0000-0001-7199-4784)
- Yan Yan (ORCID: https://orcid.org/0000-0002-0455-6212)
- Shijie Li (ORCID: https://orcid.org/0000-0003-3368-6785)
- Peng Yuan (ORCID: https://orcid.org/0009-0006-5438-2368)
- Zhonghua Ma (ORCID: https://orcid.org/0000-0001-7077-582X)
Institutions
- Peking University (CN)
- Peking University Cancer Hospital (CN)
Publication Details
- Journal
- International Journal of General Medicine
- Published
- 2024-12-01
- DOI
- https://doi.org/10.2147/ijgm.s481127
- Citations
- 3
- Primary Topic
- Colorectal Cancer Screening and Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.85
Funders
- Peking University
- Beijing Municipal Administration of Hospitals