Correlating Deep Learning-Based Automated Reference Kidney Histomorphometry with Patient Demographics and Creatinine.

ABSTRACT:

Background

Reference histomorphometric data of healthy human kidneys are largely lacking due to laborious quantitation requirements. Correlating histomorphometric features with clinical parameters through machine learning approaches can provide valuable information about natural population variance. To this end, we leveraged deep learning, computational image analysis, and feature analysis to investigate the relationship of histomorphometry with patient age, sex, and serum creatinine (SCr) in a multinational set of reference kidney tissue sections.

Methods

A panoptic segmentation neural network was developed and used to segment viable and sclerotic glomeruli, cortical and medullary interstitia, tubules, and arteries/arterioles in the digitized images of 79 periodic ac

SUBMITTER: Ginley B 

PROVIDER: S-EPMC10245721 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature