Deep Learning-Based Model Significantly Improves Diagnostic Performance for Assessing Renal Histopathology in Lupus Glomerulonephritis.

ABSTRACT:

Background

Assessment of glomerular lesions and structures plays an essential role in understanding the pathological diagnosis of glomerulonephritis and prognostic evaluation of many kidney diseases. Renal pathophysiological assessment requires novel high-throughput tools to conduct quantitative, unbiased, and reproducible analyses representing a central readout. Deep learning may be an effective tool for glomerulonephritis pathological analysis.

Methods

We developed a murine renal pathological system (MRPS) model to objectify the pathological evaluation via the deep learning method on whole-slide image (WSI) segmentation and feature extraction. A convolutional neural network model was used for accurate segmentation of glomeruli and glomerular cells of periodic acid-Schiff-s

SUBMITTER: Shen L 

PROVIDER: S-EPMC9386416 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature