AI-driven glomerular morphology quantification: a novel pipeline for assessing basement membrane thickness and podocyte foot process effacement in kidney diseases.

Journal: Computer methods and programs in biomedicine
Published Date: May 7, 2025

Abstract

BACKGROUND AND OBJECTIVE: Measuring the thickness of the glomerular basement membrane (GBM) and assessing the percentage of podocyte foot process effacement (%PFPE) are important for diagnosing non-neoplastic kidney diseases. However, when performed manually by nephropathologists using electron microscopy (EM) images, these assessments are hindered by the lack of universally standardized guidelines, leading to technical challenges. We have developed a novel deep learning (DL)-based pipeline which has the potential to reduce human error and enhance the consistency and efficiency of GBMs and %PFPE quantifications.

Authors

  • Michifumi Yamashita
    Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA.
  • Natalia Piaseczna
    Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland.
  • Akira Takahashi
    Department of Nephrology, Hiroshima University Hospital, Hiroshima, Japan.
  • Daisuke Kiyozawa
    Department of Anatomic Pathology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
  • Narihito Tatsumoto
    Department of Nephrology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan.
  • Shohei Kaneko
    Department of Nephrology, Saitama Citizens Medical Center, Saitama, Japan.
  • Natalia Zurek
    F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Arkadiusz Gertych

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