ArticleJournal of nephrology2022
Assessment of glomerular morphological patterns by deep learning algorithms.
Article in Journal of nephrology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Streamlining the Histopathologic Workflow in Diabetic Kidney Disease with Artificial Intelligence.Journal of the American Society of Nephrology : JASN · 2026Article
- Renal-AI: A Deep Learning Platform for Multi-Scale Detection of Renal Ultrastructural Features in Electron Microscopy Images.Diagnostics (Basel, Switzerland) · 2026Article
- Zebra bodies recognition by artificial intelligence (ZEBRA): a computational tool for Fabry nephropathy.Scientific reports · 2026Article
- 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
- AI-Based System for Analysis of Electron Microscope Images in Glomerular Disease.JAMA network open · 2025Article
- Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases.Bioengineering (Basel, Switzerland) · 2025Review
- Classification of primary glomerulonephritis using machine learning models: a focus on IgA nephropathy prediction.BMC nephrology · 2025Article
- Self-Supervised Learning for Feature Extraction from Glomerular Images and Disease Classification with Minimal Annotations.Journal of the American Society of Nephrology : JASN · 2025Article
- Automated scoring of glomerular injury in TNS2-deficient nephropathy.Experimental animals · 2024Article
- Vision transformer introduces a new vitality to the classification of renal pathology.BMC nephrology · 2024Article
- [Advances in computational quantitative nephropathology].Pathologie (Heidelberg, Germany) · 2024Review
- Recognition of intraglomerular histological features with deep learning in protocol transplant biopsies and their association with kidney function and prognosis.Clinical kidney journal · 2024Article
- Classification of Glomerular Pathology Images in Children Using Convolutional Neural Networks with Improved SE-ResNet Module.Interdisciplinary sciences, computational life sciences · 2023Article
- A Spatially Guided Machine-Learning Method to Classify and Quantify Glomerular Patterns of Injury in Histology Images.Journal of imaging · 2023Article
- Prediction of stability coefficient of open-pit mine slope based on artificial intelligence deep learning algorithm.Scientific reports · 2023Article
- Artificial intelligence in renal pathology: Current status and future.Biomolecules & biomedicine · 2023Review
- Current progress in artificial intelligence-assisted medical image analysis for chronic kidney disease: A literature review.Computational and structural biotechnology journal · 2023Review
- Artificial Intelligence-Assisted Renal Pathology: Advances and Prospects.Journal of clinical medicine · 2022Review
- Natural Language Processing in Diagnostic Texts from Nephropathology.Diagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCompilation of different morphological lesion signatures is characteristic of renal pathology. Previous studies have documented the potential value of artificial intelligence (AI) in recognizing relatively clear-cut glomerular structures and patterns, such as segmental or global sclerosis or mesangial hypercellularity. This study aimed to test the capacity of deep learning algorithms to recognize complex glomerular structural changes that reflect common diagnostic dilemmas in nephropathology.
methodsFor this purpose, we defined nine classes of glomerular morphological patterns and trained twelve convolutional neuronal network (CNN) models on these. The two-step training process was done on a first dataset defined by an expert nephropathologist (12,253 images) and a second consensus dataset (11,142 images) defined by three experts in the field.
resultsThe efficacy of CNN training was evaluated using another set with 180 consensus images, showing convincingly good classification results (kappa-values 0.838-0.938). Furthermore, we elucidated the image areas decisive for CNN-based decision making by class activation maps. Finally, we demonstrated that the algorithm could decipher glomerular disease patterns coinciding in a single glomerulus (e.g. necrosis along with mesangial and endocapillary hypercellularity).
conclusionsIn summary, our model, focusing on glomerular lesions detectable by conventional microscopy, is the first sui generis to deploy deep learning as a reliable and promising tool in recognition of even discrete and/or overlapping morphological changes. Our results provide a stimulus for ongoing projects that integrate further input levels next to morphology (such as immunohistochemistry, electron microscopy, and clinical information) to develop a novel tool applicable for routine diagnostic nephropathology.
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