Explainable Biomarkers for Automated Glomerular and Patient-Level Disease Classification.

ABSTRACT: Pathologists use multiple microscopy modalities to assess renal biopsy specimens. Besides usual diagnostic features, some changes are too subtle to be properly defined. Computational approaches have the potential to systematically quantitate subvisual clues, provide pathogenetic insight, and link to clinical outcomes. To this end, a proof-of-principle study is presented demonstrating that explainable biomarkers through machine learning can distinguish between glomerular disorders at the light-microscopy level. The proposed system used image analysis techniques and extracted 233 explainable biomarkers related to color, morphology, and microstructural texture. Traditional machine learning was then used to classify minimal change disease (MCD), membranous nephropathy (MN), and thin basement m

SUBMITTER: Basso MN 

PROVIDER: S-EPMC9034815 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature