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Development of an automated estimation of foot process width using deep learning
in kidney biopsies from patients with Fabry, minimal change, and diabetic kidney
diseases.
Authors Smerkous D, Mauer M, T酶ndel C, Svarstad E, Gubler MC, Nelson RG, Oliveira JP,
Sargolzaeiaval F, Najafian B
Submitted By Behzad Najafian on 10/16/2023
Status Published
Journal Kidney international
Year 2023
Date Published 9/1/2023
Volume : Pages Not Specified : Not Specified
PubMed Reference
Abstract Podocyte injury plays a key role in pathogenesis of many kidney diseases with
increased podocyte foot process width (FPW), an important measure of podocyte
injury. Unfortunately, there is no consensus on the best way to estimate FPW and
unbiased stereology, the current gold standard, is time consuming and not widely
available. To address this, we developed an automated FPW estimation technique
using deep learning. A U-Net architecture variant model was trained to
semantically segment the podocyte-glomerular basement membrane interface and
filtration slits. Additionally, we employed a post-processing computer vision
approach to accurately estimate FPW. A custom segmentation utility was also
created to manually classify these structures on digital electron microscopy
(EM) images and to prepare a training dataset. The model was applied to EM
images of kidney biopsies from 56 patients with Fabry disease, 15 with type 2
diabetes, 10 with minimal change disease, and 17 normal individuals. The results
were compared with unbiased stereology measurements performed by expert
technicians unaware of the clinical information. FPW measured by deep learning
and by the expert technicians were highly correlated and not statistically
different in any of the studied groups. A Bland-Altman plot confirmed
interchangeability of the methods. FPW measurement time per biopsy was
substantially reduced by deep learning. Thus, we have developed a novel
validated deep learning model for FPW measurement on EM images. The model is
accessible through a cloud-based application making calculation of this
important biomarker more widely accessible for research and clinical
applications.

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