A deep learning approach for gastroscopic manifestation recognition based on Kyoto Gastritis Score
Our study demonstrates the potential of deep learning approaches on gastric manifestation recognition over junior, even senior endoscopists. Thus, the deep learning approach holds potential as an auxiliary tool, although prospective validation is still needed to assess its clinical applicability.
Authors
- Jing Zhang (ORCID: https://orcid.org/0000-0002-5762-7803)
- Xiaoya Fan (ORCID: https://orcid.org/0000-0002-5002-6968)
- Zhenyang Ge (ORCID: https://orcid.org/0000-0001-6535-8634)
- Ao Liu (ORCID: https://orcid.org/0000-0002-8412-6414)
- Xilin Zhang (ORCID: https://orcid.org/0000-0003-0449-934X)
- Zhong Wang (ORCID: https://orcid.org/0009-0003-5602-1003)
- Jiaxin Zhong
- Zilu Wang (ORCID: https://orcid.org/0009-0004-0144-6888)
Institutions
- Dalian University of Technology (CN)
- Dalian Municipal Central Hospital (CN)
- China Medical University (CN)
Publication Details
- Journal
- Annals of Medicine
- Published
- 2024-11-05
- DOI
- https://doi.org/10.1080/07853890.2024.2418963
- Citations
- 8
- Primary Topic
- Helicobacter pylori-related gastroenterology studies
- Type
- article
- Field-Weighted Citation Impact
- 4.58
Funders
- Fundamental Research Funds for the Central Universities