Deep Learning Models for Anatomical Location Classification in Esophagogastroduodenoscopy Images and Videos: A Quantitative Evaluation with Clinical Data
This study demonstrates the feasibility of predicting the gastrointestinal tract locations during gastroscopy and suggests a promising path for the development of advanced diagnostic aids to assist clinicians. Furthermore, the location information generated by this model can be leveraged in future technologies, such as automated report generation and supporting follow-up examinations for patients.
Authors
- Gi Pyo Lee (ORCID: https://orcid.org/0000-0002-9785-3776)
- Seong Min Kang (ORCID: https://orcid.org/0000-0002-8952-8500)
- Kyoung Oh Kim (ORCID: https://orcid.org/0000-0002-5365-2550)
- Kwang Gi Kim (ORCID: https://orcid.org/0000-0001-9714-6038)
- Young Jae Kim (ORCID: https://orcid.org/0000-0003-0443-0051)
Institutions
- Gachon University (KR)
- Gachon University Gil Medical Center (KR)
- Deleted Institution
Publication Details
- Journal
- Diagnostics
- Published
- 2024-10-23
- DOI
- https://doi.org/10.3390/diagnostics14212360
- Citations
- 3
- Primary Topic
- Esophageal Cancer Research and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.72
Funders
- Gachon University
- National IT Industry Promotion Agency
- Ministry of Science and ICT, South Korea