ReviewKidney international reports2026
Artificial Intelligence in Kidney Transplant Pathology: Current Evidence, Limitations, and Relevance to the Banff Framework.
Review in Kidney international reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
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Abstract
The Banff Classification, established in 1991, provides a global standard for diagnosing and grading kidney transplant pathology, evolving through regular consensus meetings to incorporate new advances. Initially designed to address the lack of uniformity in renal allograft biopsy reporting, the Banff system now integrates semiquantitative scores for acute and chronic lesions. Recent progress in artificial intelligence (AI) and deep learning (DL) has accelerated quantitative analysis in kidney histology, particularly for interstitial fibrosis and tubular atrophy (IFTA) and inflammation, reducing interobserver variability and supporting prognostic assessment. Targeted algorithms such as positive pixel count (PPC) and DL-based segmentation models have demonstrated robust performance in identifying renal compartments and quantifying injury; and AI-assisted approaches for vascular lesions and immune cell profiling show promise but are limited in data diversity, stain standardization, and external validation. Additional metrics, such as nephron size, IFTA foci density, and mesangial expansion (ME), have been explored, with computational metrics and AI models showing potential for improved reproducibility and clinical relevance. Despite concerns about the implications of AI in pathology, optimism prevails regarding its ability to augment human expertise and enhance diagnostic precision. Ongoing work within the Banff Digital Pathology Working Group aims to extend automated assessment to additional Banff lesions, including glomerulitis, peritubular capillaritis, arteritis, and tubulitis. As these tools mature, they may enable more comprehensive quantitative characterization of transplant biopsies and facilitate exploration of histopathologic patterns that have been difficult to study. The Banff system's iterative evolution and embrace of AI-driven digital pathology underscore its enduring impact and promise in kidney transplant medicine.
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