Assessment of glomerular morphological patterns by deep learning algorithms.

Journal: Journal of nephrology
PMID: 34982414

Abstract

BACKGROUND: Compilation of different morphological lesion signatures is characteristic of renal pathology. Previous studies have documented the potential value of artificial intelligence (AI) in recognizing relatively clear-cut glomerular structures and patterns, such as segmental or global sclerosis or mesangial hypercellularity. This study aimed to test the capacity of deep learning algorithms to recognize complex glomerular structural changes that reflect common diagnostic dilemmas in nephropathology.

Authors

  • Cleo-Aron Weis
    Institute of Pathology, University Medical Center Mannheim, Mannheim, Germany.
  • Jan Niklas Bindzus
    Institute of Pathology, University Medical Centre Mannheim, University of Heidelberg, 68167, Mannheim, Germany.
  • Jonas Voigt
    Institute of Pathology, University Medical Centre Mannheim, University of Heidelberg, 68167, Mannheim, Germany.
  • Marlen Runz
    Institute of Pathology, University Medical Centre Mannheim, University of Heidelberg, 68167, Mannheim, Germany.
  • Svetlana Hertjens
    Institute of Medical Statistics and Biometry, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
  • Matthias M Gaida
    Institute of Pathology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131, Mainz, Germany.
  • Zoran V Popovic
    Institute of Pathology, Medical Faculty Mannheim of Heidelberg University, University Medical Center Mannheim, Mannheim, Germany.
  • Stefan Porubsky
    Institute of Pathology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.

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