ReviewNature reviews. Nephrology2020
Digital pathology and computational image analysis in nephropathology.
Review in Nature reviews. Nephrology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 130 papers, 2 of them syntheses that pooled it.
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
130 citing papers in PubMed, 2 syntheses or guidelines pooled it, 249 citations in OpenAlex.
- Genetic and environmental factors associated with alteration of filtration slit proteins and their functions: a scoping review.Frontiers in nephrology · 2025Pooled it
- Artificial intelligence applications for pre-implantation kidney biopsy pathology practice: a systematic review.Journal of nephrology · 2022Pooled it
- A nine-gene diagnostic model for IgA nephropathy based on multi-cohort machine learning: integrating gene expression and immunohistochemical validation.Renal failure · 2026Article
- A guide for the deployment, validation, and accreditation of clinical digital pathology tools.Journal of pathology informatics · 2026Article
- Individualised treatment effects of corticosteroids in IgA nephropathy.EBioMedicine · 2026Article
- Interobserver agreement analysis among renal pathologists in classification of lupus nephritis using a digital pathology image dataset: after a third evaluation.Kidney research and clinical practice · 2026Article
- Renal Involvement in Pediatric Small-Vessel Vasculitis: A Comprehensive Review of Clinical Impact, Diagnosis, and Management.Medical sciences (Basel, Switzerland) · 2026Review
- [Histology and more-modern nephropathology].Innere Medizin (Heidelberg, Germany) · 2026Review
- Podometrics as a translational platform for kidney health and disease: Oshima award address 2025.Clinical and experimental nephrology · 2026Review
- Deep learning-based pathomics signature predicts prognosis and treatment response in gastric cancer: a multicenter retrospective study.NPJ precision oncology · 2026Article
- Survival risk prediction for patients with lung adenocarcinoma containing micropapillary components based on weakly supervised pathomics.Journal of thoracic disease · 2026Article
- Computational Characterization of Lymphocyte Topology on Whole Slide Images of Glomerular Diseases.Journal of the American Society of Nephrology : JASN · 2026Article
- Deep learning-driven immunohistochemical analysis of renal lymphatics for chronic kidney disease: bioinformatic and histopathological study.Journal of pathology and translational medicine · 2026Article
- Quantitative Texture Analysis of Cervical Cytology Identifies Endometrial Lesions in Atypical Glandular Cells on Liquid-Based Cytology: A Pilot Study.Diagnostics (Basel, Switzerland) · 2026Article
- Quantification of interstitial fibrosis in digitized kidney biopsies using deep neural networks.Diagnostic pathology · 2026Article
- PathViT Model for Automated Disease Classification from Skeletal Muscle Histopathology.The American journal of pathology · 2026Article
- A multi-task deep learning framework for simultaneous prediction of microsatellite instability and tumor mutational burden in gastric cancer from histopathological images.Frontiers in oncology · 2026Article
- Development and evaluation of machine learning models for predicting relapse in idiopathic nephrotic syndrome.Frontiers in endocrinology · 2026Article
- Efficient and explainable histopathology for cancer detection using dual-teacher distillation and integrated gradients.Frontiers in medicine · 2026Article
- Current and Future Applications of AI-Driven Predictive Modeling and a Proposed Framework for AI-Bioprognostics in Kidney Care.Risk management and healthcare policy · 2026Review
70 more citing papers are in PubMed but not listed here.
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 at 4 institutions in 1 country.
Funding
Abstract
The emergence of digital pathology - an image-based environment for the acquisition, management and interpretation of pathology information supported by computational techniques for data extraction and analysis - is changing the pathology ecosystem. In particular, by virtue of our new-found ability to generate and curate digital libraries, the field of machine vision can now be effectively applied to histopathological subject matter by individuals who do not have deep expertise in machine vision techniques. Although these novel approaches have already advanced the detection, classification, and prognostication of diseases in the fields of radiology and oncology, renal pathology is just entering the digital era, with the establishment of consortia and digital pathology repositories for the collection, analysis and integration of pathology data with other domains. The development of machine-learning approaches for the extraction of information from image data, allows for tissue interrogation in a way that was not previously possible. The application of these novel tools are placing pathology centre stage in the process of defining new, integrated, biologically and clinically homogeneous disease categories, to identify patients at risk of progression, and shifting current paradigms for the treatment and prevention of kidney diseases.
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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.