eClinicalMedicine | AI-Assisted Diagnosis of Glomerular Nephritis

This study developed and validated an artificial intelligence (AI)-assisted diagnostic model for automated diagnosis of glomerular nephritis (GN) based on renal biopsy, achieving high accuracy and stability. Through multi-center data validation, the article demonstrates the AI model's robust performance in diagnosing four GN types (IgA nephropathy, membranous nephropathy, focal segmental glomerulosclerosis, and minimal change disease), providing an essential tool to enhance pathologists' diagnostic efficiency and consistency.

 

Literature Overview
This article, 'Artificial intelligence-assisted diagnosis of glomerular nephritis using a pathological image analysis approach: a multicentre model development and validation study', published in eClinicalMedicine, reviews the development and validation process of an AI-assisted diagnostic model for glomerular nephritis. Through multi-center research utilizing 106,988 glomerular microscopic images, the study constructed an integrated model comprising three components: glomerular localization, lesion feature extraction, and patient-level classification, achieving an overall performance F1-score exceeding 85%. The research further explored a dual-branch AI model incorporating light microscopy and immunofluorescence data to improve diagnostic precision.

Background Knowledge
Glomerular nephritis (GN) represents a group of diseases characterized by glomerular inflammation, serving as a major cause of end-stage kidney disease, particularly in developing countries. Currently, GN diagnosis relies on histopathological analysis of renal biopsies, a process requiring experienced pathologists to manually identify glomerular lesions but suffering from subjectivity, time consumption, and poor reproducibility. While AI applications in digital pathology have demonstrated superior capabilities in diagnosing cancers like breast and lung cancer, a unified, efficient framework for GN diagnosis remains lacking. This study pioneers AI application in GN pathology diagnosis by constructing an automated, reproducible tool to address manual reading limitations. Although prior research has explored AI potential in glomerular segmentation and lesion detection, large-scale, multi-center validation remains absent. By training on over 100,000 images, this study validated the AI model's stability in real-world data while identifying its limitations regarding race, disease subtypes, and multi-staining approaches, providing directions for future research.

 

 

Research Methods and Experiments
The study employed a multi-center design, collecting pathological image data from 6,682 renal biopsy patients, including 1,235 training cases, 312 internal validation cases, and two external validation cohorts comprising 2,483 and 2,652 cases respectively. All images underwent quality control and standardization, retaining only Periodic Acid-Silver Methenamine (PASM)-stained light microscopy images. The AI model contains three core modules: GloSNet for glomerular localization (image segmentation), feature fusion module for extracting and integrating glomerular lesion features, and patient-level classification module for final diagnosis. Evaluation metrics included F1-score, precision, recall, and accuracy. Additionally, a dual-branch model combining immunofluorescence (IF) images was explored to enhance diagnostic accuracy.

Key Conclusions and Perspectives

• The AI model achieved F1-scores of 83.86% in external validation cohort 1 and 85.45% in cohort 2, demonstrating high diagnostic consistency across different datasets.
• The glomerular segmentation module (GloSNet) showed excellent performance at both pixel and glomerular levels, achieving F1-scores of 93.01% and 96.10% respectively, indicating high accuracy in automated glomerular identification.
• The feature fusion strategy based on kernel density estimation effectively integrated diverse lesion features, enhancing multi-category GN classification performance, particularly achieving 97.13% F1-score for membranous nephritis (MN).
• The dual-branch AI model incorporating immunofluorescence data improved F1-score to 91.00 in four-classification tasks, demonstrating IF data's effectiveness in enhancing AI diagnostic capabilities.
• Through attention heatmap visualization, the research team confirmed the AI model's precise identification of GN-related pathological features, such as MN's basement membrane thickening and FSGS's sclerotic regions.

Research Significance and Prospects
This study demonstrates AI's high accuracy in glomerular nephritis diagnosis for the first time, providing pathologists with an objective, reproducible auxiliary tool. Future research should expand to more races and GN subtypes while optimizing model interpretability to improve clinical applicability. The research team also recommends integrating multi-staining methods and multi-omics data to enhance generalization capability, enabling broader applications across global medical centers.

 

 

Conclusion
This research successfully developed an AI-assisted diagnostic model for glomerular nephritis based on renal biopsy light microscopy images, achieving efficient classification of four GN types (IgAN, MN, FSGS, MCD). The model demonstrated excellent performance in two external validation cohorts with F1-scores exceeding 83%. Through visualization analysis, the team confirmed the AI model's ability to identify GN-associated pathological features. Although currently limited by race and disease subtypes, this tool significantly reduces pathologists' workload while improving diagnostic consistency. Future directions include expanding to more subtypes, integrating multi-staining methods, enhancing model interpretability, and exploring real-time diagnostic and telemedicine applications.

 

Sheng Nie, Nan Jia, Haobo Chen, Qi Zhang, and Fan Fan Hou. Artificial intelligence-assisted diagnosis of glomerular nephritis using a pathological image analysis approach: a multicentre model development and validation study. eClinicalMedicine.