A deep learning and morphometric hybrid model for automated quantification of kidney interstitial fibrosis in trichrome-stained whole-slide image
TRI_IF provides an accurate, reproducible, and clinically meaningful method for quantifying IF from TRI-stained WSIs. By eliminating the need for manual annotations and reducing interobserver variability, this approach offers a scalable solution for both clinical and research applications in nephropathology.
Authors
- Joachim H. Ix (ORCID: https://orcid.org/0000-0002-8084-9869)
- Natalia Ortuño
- Shilpa Bharati
- Hemant Kulkarni (ORCID: https://orcid.org/0000-0001-6157-2668)
- Ambarish Athavale
- Tushar Patel
Institutions
- Texas A&M University – San Antonio (US)
- University of California San Diego (US)
- University of Illinois Chicago (US)
Publication Details
- Journal
- Journal of Pathology Informatics
- Published
- 2026-02-12
- DOI
- https://doi.org/10.1016/j.jpi.2026.100649
- Primary Topic
- Chronic Kidney Disease and Diabetes
- Type
- article
- Field-Weighted Citation Impact
- 0.00