ArticleKidney international2021
Multistain segmentation of renal histology: first steps toward artificial intelligence-augmented digital nephropathology.
Article in Kidney international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
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Who cites it
9 citing papers in PubMed, 11 citations in OpenAlex.
- A Unified Deep Learning Framework for Instance Segmentation Across Diverse Cytological Stains.Cytopathology : official journal of the British Society for Clinical Cytology · 2026Article
- Non-invasive assessment techniques for renal fibrosis: advances and perspectives.Renal failure · 2025Review
- Deep learning-based quantitative analysis of glomerular morphology in IgA nephropathy whole slide images and its prognostic implications.Scientific reports · 2025Article
- An initiative on digital nephrology: the Kidney Imageomics Project.National science review · 2025Article
- Artificial intelligence-assisted quantification and assessment of whole slide images for pediatric kidney disease diagnosis.Bioinformatics (Oxford, England) · 2024Article
- Development of an automated estimation of foot process width using deep learning in kidney biopsies from patients with Fabry, minimal change, and diabetic kidney diseases.Kidney international · 2024Article
- Artificial intelligence in renal pathology: Current status and future.Biomolecules & biomedicine · 2023Review
- Integration of artificial intelligence and multi-omics in kidney diseases.Fundamental research · 2023Review
- Definition, diagnosis and clinical management of non-obstructive kidney dysplasia: a consensus statement by the ERKNet Working Group on Kidney Malformations.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2022Article
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI), and particularly deep learning (DL), are showing great potential in improving pathology diagnostics in many aspects, 1 of which is the segmentation of histology into (diagnostically) relevant compartments. Although most current studies focus on AI and DL in oncologic pathology, an increasing number of studies explore their application to nephropathology, including the study published in this issue of Kidney International by Jayapandian et al.
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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.