GloPath: An Entity-Centric Foundation Model for Glomerular Lesion Assessment and Clinicopathological Insights

2026-03-03

AI Summary

This study addresses the challenge of accurately assessing glomerular lesions, which exhibit high morphological heterogeneity and complex fine-grained injury patterns that existing AI methods struggle to capture and link to clinical metrics. To this end, we propose the first large-scale self-supervised foundation model built upon individual glomeruli as fundamental units, integrating multi-scale and multi-view learning with instance segmentation. Trained on over one million glomeruli, the model supports few-shot classification, cross-modal diagnosis, and morphological–clinical association analysis. It outperforms current approaches in 42 out of 52 tasks, achieves a real-world lesion detection ROC-AUC of 91.51%, and identifies 224 significant morphology–clinical variable associations, demonstrating both high diagnostic accuracy and strong clinical translatability.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Data transparency and provenance

Abstract

Glomerular pathology is central to the diagnosis and prognosis of renal diseases, yet the heterogeneity of glomerular morphology and fine-grained lesion patterns remain challenging for current AI approaches. We present GloPath, an entity-centric foundation model trained on over one million glomeruli extracted from 14,049 renal biopsy specimens using multi-scale and multi-view self-supervised learning. GloPath addresses two major challenges in nephropathology: glomerular lesion assessment and clinicopathological insights discovery. For lesion assessment, GloPath was benchmarked across three independent cohorts on 52 tasks, including lesion recognition, grading, few-shot classification, and cross-modality diagnosis-outperforming state-of-the-art methods in 42 tasks (80.8%). In the large-scale real-world study, it achieved an ROC-AUC of 91.51% for lesion recognition, demonstrating strong robustness in routine clinical settings. For clinicopathological insights, GloPath systematically revealed statistically significant associations between glomerular morphological parameters and clinical indicators across 224 morphology-clinical variable pairs, demonstrating its capacity to connect tissue-level pathology with patient-level outcomes. Together, these results position GloPath as a scalable and interpretable platform for glomerular lesion assessment and clinicopathological discovery, representing a step toward clinically translatable AI in renal pathology.

Problem

• glomerular lesion assessment
• clinicopathological insights
• renal pathology
• morphological heterogeneity
• fine-grained lesion patterns

Innovation

• entity-centric foundation model
• glomerular lesion assessment
• multi-scale self-supervised learning
• clinicopathological correlation
• renal pathology AI