ArticleBiomedical journal2022
Glomerular disease classification and lesion identification by machine learning.
Article in Biomedical journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis 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
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- Artificial intelligence-powered renal pathology for glomerular disease classification: a systematic review and meta-analysis.Renal failure · 2026Pooled it
- Machine learning prediction of childhood nephrotic syndrome outcomes.Pediatric nephrology (Berlin, Germany) · 2026Observational
- Machine learning-based model for identifying liver injury in patients with thyroid-associated ophthalmopathy.International ophthalmology · 2026Article
- A brief review of some artificial intelligence methods in nephrology.Pediatric nephrology (Berlin, Germany) · 2026Review
- Glo-VLMs: Leveraging Vision-Language Models for Fine-Grained Diseased Glomerulus Classification.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Morphometry-based detection of deep learning faults in glomerular segmentation.bioRxiv : the preprint server for biology · 2025Article
- Multimodal deep learning improving the accuracy of pathological diagnoses for membranous nephropathy.Renal failure · 2025Article
- AI-based pathomics in kidney diseases: progress and application.Renal failure · 2025Review
- Classification of primary glomerulonephritis using machine learning models: a focus on IgA nephropathy prediction.BMC nephrology · 2025Article
- Permutation-Invariant Cascaded Attentional Set Operator for Computational Nephropathology.Kidney360 · 2025Article
- GLAM: Glomeruli Segmentation for Human Pathological Lesions using Adapted Mouse Model.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Explainability of a Deep Learning-Based Classification Model for Antineutrophil Cytoplasmic Autoantibody-Associated Glomerulonephritis.Kidney international reports · 2025Article
- Leveraging advanced feature extraction for improved kidney biopsy segmentation.Frontiers in medicine · 2025Article
- GNCnn: A QuPath extension for glomerulosclerosis and glomerulonephritis characterization based on deep learning.Computational and structural biotechnology journal · 2025Article
- Automated scoring of glomerular injury in TNS2-deficient nephropathy.Experimental animals · 2024Article
- Article
- Machine learning-based diagnosis and prognosis of IgAN: A systematic review and meta-analysis.Heliyon · 2024Review
- Systemic lupus in the era of machine learning medicine.Lupus science & medicine · 2024Review
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClassification of glomerular diseases and identification of glomerular lesions require careful morphological examination by experienced nephropathologists, which is labor-intensive, time-consuming, and prone to interobserver variability. In this regard, recent advance in machine learning-based image analysis is promising.
methodsWe combined Mask Region-based Convolutional Neural Networks (Mask R-CNN) with an additional classification step to build a glomerulus detection model using human kidney biopsy samples. A Long Short-Term Memory (LSTM) recurrent neural network was applied for glomerular disease classification, and another two-stage model using ResNeXt-101 was constructed for glomerular lesion identification in cases of lupus nephritis.
resultsThe detection model showed state-of-the-art performance on variedly stained slides with F1 scores up to 0.944. The disease classification model showed good accuracies up to 0.940 on recognizing different glomerular diseases based on H&E whole slide images. The lesion identification model demonstrated high discriminating power with area under the receiver operating characteristic curve up to 0.947 for various glomerular lesions. Models showed good generalization on external testing datasets.
conclusionThis study is the first-of-its-kind showing how each step of kidney biopsy interpretation carried out by nephropathologists can be captured and simulated by machine learning models. The models were integrated into a whole slide image viewing and annotating platform to enable nephropathologists to review, correct, and confirm the inference results. Further improvement on model performances and incorporating inputs from immunofluorescence, electron microscopy, and clinical data might realize actual clinical use.
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.