ArticleJournal of the American Society of Nephrology : JASN2019
Computational Segmentation and Classification of Diabetic Glomerulosclerosis.
Article in Journal of the American Society of Nephrology : JASN, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 114 papers, 2 of them syntheses that pooled it.
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Who cites it
114 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Time for a full digital approach in nephropathology: a systematic review of current artificial intelligence applications and future directions.Journal of nephrology · 2024Pooled it
- Artificial intelligence applications for pre-implantation kidney biopsy pathology practice: a systematic review.Journal of nephrology · 2022Pooled it
- Artificial Intelligence in Kidney Transplant Pathology: Current Evidence, Limitations, and Relevance to the Banff Framework.Kidney international reports · 2026Review
- Bibliometrics and visualization analysis: Big data research on diabetic kidney disease from 2000 to 2025.Medicine · 2026Article
- Reappraising Kidney Biopsy in Diabetic Kidney Disease: Histopathology, Clinical Course, and the Future of Precision Nephrology.Journal of clinical medicine · 2026Review
- Mitigation of renal tubular injury by SIRT6 may improve individual outcomes in diabetic kidney disease-potential mechanisms involving epigenetic repression of inflammatory responses.Journal of advanced research · 2026Article
- Rapid slide-level assessment of glomerular yield using virtual staining of unstained renal biopsy sections.Diagnostic pathology · 2026Article
- Association between visceral adiposity indices and retinal microvascular parameters stratified by urinary protein status.BMC ophthalmology · 2026Article
- Streamlining the Histopathologic Workflow in Diabetic Kidney Disease with Artificial Intelligence.Journal of the American Society of Nephrology : JASN · 2026Article
- A deep learning and morphometric hybrid model for automated quantification of kidney interstitial fibrosis in trichrome-stained whole-slide image.Journal of pathology informatics · 2026Article
- Computational Characterization of Lymphocyte Topology on Whole Slide Images of Glomerular Diseases.Journal of the American Society of Nephrology : JASN · 2026Article
- Glomeruli detection and classification in histopathological images using deep learning semantic segmentation.BMC medical imaging · 2026Article
- GlomNet: glomeruli segmentation network for WSI with diverse staining protocols for kidney disease diagnosis.Frontiers in medicine · 2026Article
- Non-diabetic Kidney Disease in Type 2 Diabetes: From Kidney Biopsy to Precision Medicine.Cureus · 2026Review
- Morphometry-based detection of deep learning faults in glomerular segmentation.bioRxiv : the preprint server for biology · 2025Article
- AI-based pathomics in kidney diseases: progress and application.Renal failure · 2025Review
- Transformer-based multiclass segmentation pipeline for basic kidney histology.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
- Chronic Changes on Kidney Histology by a Multiclass Artificial Intelligence Model.Kidney international reports · 2025Article
54 more citing papers are in PubMed but not listed here.
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Authors and funding
12 authors.
Funding
Abstract
backgroundPathologists use visual classification of glomerular lesions to assess samples from patients with diabetic nephropathy (DN). The results may vary among pathologists. Digital algorithms may reduce this variability and provide more consistent image structure interpretation.
methodsWe developed a digital pipeline to classify renal biopsies from patients with DN. We combined traditional image analysis with modern machine learning to efficiently capture important structures, minimize manual effort and supervision, and enforce biologic prior information onto our model. To computationally quantify glomerular structure despite its complexity, we simplified it to three components consisting of nuclei, capillary lumina and Bowman spaces; and Periodic Acid-Schiff positive structures. We detected glomerular boundaries and nuclei from whole slide images using convolutional neural networks, and the remaining glomerular structures using an unsupervised technique developed expressly for this purpose. We defined a set of digital features which quantify the structural progression of DN, and a recurrent network architecture which processes these features into a classification.
resultsOur digital classification agreed with a senior pathologist whose classifications were used as ground truth with moderate Cohen's kappa κ = 0.55 and 95% confidence interval [0.50, 0.60]. Two other renal pathologists agreed with the digital classification with κ
conclusionsComputationally derived, histologic image features hold significant diagnostic information that may augment clinical diagnostics.
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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.