Development and Validation of a Deep Learning Model to Quantify Glomerulosclerosis in Kidney Biopsy Specimens

Abstract

Key Points Question Can a deep neural network decrease likelihood of unnecessary donor kidney discard by precisely quantifying percent global glomerulosclerosis on whole-slide images of hematoxylin-eosin–stained biopsy specimens? Findings In this prognostic study of 83 donor kidneys, a deep neural network segmented normal and globally sclerotic glomeruli in whole-slide images to quantify percent global glomerulosclerosis with higher performance than pathologists. Model accuracy further increased by pooling multiple sections, resulting in decreased likelihood of erroneous organ discard by 37%. Meaning This study’s findings suggest that deep learning methods may help prevent erroneous organ discard by performing beyond the capacity of pathologists in biopsy specimen examination.