ReviewKidney international2021
AI applications in renal pathology.
Review in Kidney international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
43 citing papers in PubMed.
- Continuing medical education in renal pathology: current practices and needs among nephrologists.BMC medical education · 2026Article
- Transforming nephrology through artificial intelligence: a state-of-the-art roadmap for clinical integration.Clinical kidney journal · 2026Review
- Artificial intelligence in genitourinary pathology.Histopathology · 2026Review
- Superior transplant recipient outcome prediction and pathology assessment using rapid deep learning applied to procurement kidney biopsies.Scientific reports · 2025Article
- Glo-In-One-v2: holistic identification of glomerular cells, tissues, and lesions in human and mouse histopathology.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Artificial intelligence-assisted diagnosis of glomerular nephritis using a pathological image analysis approach: a multicentre model development and validation study.EClinicalMedicine · 2025Article
- AI-Based System for Analysis of Electron Microscope Images in Glomerular Disease.JAMA network open · 2025Article
- Fine-grained multiclass nuclei segmentation with molecular empowered all-in-SAM model.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- "Normal" Glomerular Score Correlates With Outcomes in Antineutrophil Cytoplasmic Autoantibody-Associated Vasculitis With Glomerulonephritis.Kidney international reports · 2025Article
- Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Revolutionizing renal biopsy: the emerging role of omics and artificial intelligence in nephrology.Frontiers in medicine · 2025Review
- Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging.IS&T International Symposium on Electronic Imaging · 2025Article
- Deep learning in renal ultrasound: applications, challenges, and future outlook.Frontiers in oncology · 2025Review
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Vision transformer introduces a new vitality to the classification of renal pathology.BMC nephrology · 2024Article
- HATs: Hierarchical Adaptive Taxonomy Segmentation for Panoramic Pathology Image Analysis.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- A new era in the science and care of kidney diseases.Nature reviews. Nephrology · 2024Review
- Artificial intelligence assists identification and pathologic classification of glomerular lesions in patients with diabetic nephropathy.Journal of translational medicine · 2024Article
- Deep-learning model for evaluating histopathology of acute renal tubular injury.Scientific reports · 2024Article
- Computational pathology model to assess acute and chronic transformations of the tubulointerstitial compartment in renal allograft biopsies.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The explosive growth of artificial intelligence (AI) technologies, especially deep learning methods, has been translated at revolutionary speed to efforts in AI-assisted healthcare. New applications of AI to renal pathology have recently become available, driven by the successful AI deployments in digital pathology. However, synergetic developments of renal pathology and AI require close interdisciplinary collaborations between computer scientists and renal pathologists. Computer scientists should understand that not every AI innovation is translatable to renal pathology, while renal pathologists should capture high-level principles of the relevant AI technologies. Herein, we provide an integrated review on current and possible future applications in AI-assisted renal pathology, by including perspectives from computer scientists and renal pathologists. First, the standard stages, from data collection to analysis, in full-stack AI-assisted renal pathology studies are reviewed. Second, representative renal pathology-optimized AI techniques are introduced. Last, we review current clinical AI applications, as well as promising future applications with the recent advances in AI.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.