Enhancing the Predictions of Cytomegalovirus Infection in Severe Ulcerative Colitis Using a Deep Learning Ensemble Model: Development and Validation Study
This study demonstrates that deep learning models can effectively distinguish CMV from severe UC in endoscopic images, paving the way for early, noninvasive diagnosis and improved patient care.
Authors
- Yehyun Park (ORCID: https://orcid.org/0000-0001-8811-0631)
- Rena Lee (ORCID: https://orcid.org/0009-0003-3630-7813)
- A Reum Choe (ORCID: https://orcid.org/0000-0002-2552-7066)
- Eun Mi Song (ORCID: https://orcid.org/0000-0002-2428-1551)
- Ju Ran Byeon (ORCID: https://orcid.org/0000-0003-3188-9693)
- Sung-Ae Jung (ORCID: https://orcid.org/0000-0001-7224-2867)
- Eui Sun Jeong (ORCID: https://orcid.org/0000-0001-8569-9380)
- Seong‐Eun Kim (ORCID: https://orcid.org/0000-0002-6310-5366)
- Jin Sung Kim (ORCID: https://orcid.org/0000-0003-1415-6471)
- JeongHeon Kim (ORCID: https://orcid.org/0000-0001-9321-3117)
- So Hyun Ahn
Institutions
- Ewha Womans University (KR)
- Yonsei University (KR)
- Dongwha Pharm (South Korea) (KR)
- Ewha Womans University Medical Center (KR)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-03-11
- DOI
- https://doi.org/10.2196/64987
- Citations
- 1
- Primary Topic
- Cytomegalovirus and herpesvirus research
- Type
- article
- Field-Weighted Citation Impact
- 1.35