Machine Learning-Based Detection of Intestinal Metaplasia Identifies Clinically Missed Cases
An artificial intelligence-based quality assurance pipeline could detect missed intestinal metaplasia in as many as 1.5% of gastric biopsy specimens, highlighting a potentially addressable diagnostic gap. An automated pipeline could additionally provide quantitative information that could be informative for management.
Authors
- Katherine S. Garman (ORCID: https://orcid.org/0000-0001-7763-8465)
- William R. Jeck (ORCID: https://orcid.org/0000-0003-0257-4529)
- Jacqueline Emerson
- Wei Chen
- Fengming Chen
- Avani A Pendse
- John A Willis
Institutions
- University of North Carolina at Chapel Hill (US)
- Duke University (US)
- Duke University Hospital (US)
Publication Details
- Journal
- Archives of Pathology & Laboratory Medicine
- Published
- 2026-01-22
- DOI
- https://doi.org/10.5858/arpa.2025-0307-oa
- Primary Topic
- Gastrointestinal Bleeding Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00