ArticleDiagnostics (Basel, Switzerland)2021
Deep Learning-Based Artificial Intelligence System for Automatic Assessment of Glomerular Pathological Findings in Lupus Nephritis.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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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
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Diagnostic Performance of Machine Learning for Systemic Lupus Erythematosus: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- The application of artificial intelligence in systemic lupus erythematosus: a bibliometric analysis of current trends and future directions.Frontiers in medicine · 2026Pooled it
- Artificial neural network - an effective tool for predicting the lupus nephritis outcome.BMC nephrology · 2022Trial
- Artificial Intelligence and Machine Learning in Rheumatology and Systemic Inflammatory Diseases: From Pattern Recognition to Signal Analysis and Clinical Decision Support.Journal of clinical medicine · 2026Review
- GloPath: An Entity-Centric Foundation Model for Glomerular Lesion Assessment and Clinicopathological Insights.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- KDH-Net: Explainable Medical AI for Multiclass Kidney Disease Characterization from CT Images.Journal of clinical medicine · 2026Article
- AI-based pathomics in kidney diseases: progress and application.Renal failure · 2025Review
- Machine learning in lupus nephritis: bridging prediction models and clinical decision-making towards personalized nephrology.Frontiers in medicine · 2025Review
- Artificial Intelligence in Nephrology: Clinical Applications and Challenges.Kidney medicine · 2025Review
- Automated scoring of glomerular injury in TNS2-deficient nephropathy.Experimental animals · 2024Article
- Gene-Based Predictive Modelling for Enhanced Detection of Systemic Lupus Erythematosus Using CNN-Based DL Algorithm.Diagnostics (Basel, Switzerland) · 2024Article
- Recognition of intraglomerular histological features with deep learning in protocol transplant biopsies and their association with kidney function and prognosis.Clinical kidney journal · 2024Article
- Studying Pregnancy Outcome Risk in Patients with Systemic Lupus Erythematosus Based on Cluster Analysis.BioMed research international · 2023Article
- Artificial Intelligence-Assisted Renal Pathology: Advances and Prospects.Journal of clinical medicine · 2022Review
- Special Issue: "Machine Learning for Computer-Aided Diagnosis in Biomedical Imaging".Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
Accurate assessment of renal histopathology is crucial for the clinical management of patients with lupus nephritis (LN). However, the current classification system has poor interpathologist agreement. This paper proposes a deep convolutional neural network (CNN)-based system that detects and classifies glomerular pathological findings in LN. A dataset of 349 renal biopsy whole-slide images (WSIs) (163 patients with LN, periodic acid-Schiff stain, 3906 glomeruli) annotated by three expert nephropathologists was used. The CNN models YOLOv4 and VGG16 were employed to localise the glomeruli and classify glomerular lesions (slight/severe impairments or sclerotic lesions). An additional 321 unannotated WSIs from 161 patients were used for performance evaluation at the per-patient kidney level. The proposed model achieved an accuracy of 0.951 and Cohen's kappa of 0.932 (95% CI 0.915-0.949) for the entire test set for classifying the glomerular lesions. For multiclass detection at the glomerular level, the mean average precision of the CNN was 0.807, with 'slight' and 'severe' glomerular lesions being easily identified (F1: 0.924 and 0.952, respectively). At the per-patient kidney level, the model achieved a high agreement with nephropathologist (linear weighted kappa: 0.855, 95% CI: 0.795-0.916,
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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.