Multimodal deep learning improving the accuracy of pathological diagnoses for membranous nephropathy

• Published in: Renal failure Vol. 47; no. 1; p. 2528106
• Main Authors: Hu, Xiuxiu, Yang, Jinyue, Li, Yiping, Gong, Yuxiang, Ni, Haifeng, Wei, Qing, Yang, Minyu, Zhang, Yu, Huang, Jing, Ma, Cao, Wei, Bizhen, Yu, Kaijie, Xu, Jiayun, Xia, Siyu, Tang, Taotao, Chen, Pingsheng
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 01.12.2025; Taylor & Francis Group
• Subjects: Adult; Aged; artificial intelligence; Artificial Intelligence and Machine Learning; Biopsy; Deep Learning; Female; Fluorescent Antibody Technique; Glomerulonephritis, Membranous - diagnosis; Glomerulonephritis, Membranous - pathology; Humans; Kidney - pathology; Kidney Glomerulus - pathology; Male; Membranous nephropathy; Microscopy, Electron; Middle Aged; multimodal pathological diagnosis; renal biopsy; Reproducibility of Results; multimodal pathological diagnosis; deep learning; Membranous nephropathy; artificial intelligence; renal biopsy

Renal biopsy is the gold standard for the diagnosis of glomerular diseases including membranous nephropathy (MN), however, it faces challenges in accuracy, objectivity, and reproducibility of tissue evaluation. This study aims to develop a multimodal pathological diagnosis system to assist pathologi...