Utilizing Deep Learning to Identify Electron-Dense Deposits in Renal Biopsy Electron Microscopy Images.

Journal: American journal of nephrology
Published Date: May 19, 2025

Abstract

INTRODUCTION: Electron microscopy (EM) is a crucial technique for identifying and distinguishing the specific location of deposits within glomeruli. Manual classification of these deposit locations is not only time-consuming but also yields inconsistent results among pathologists. This study aimed to develop a deep learning-based platform to automatically classify the locations of electron-dense deposits in EM images of kidney biopsies.

Authors

  • Shuangshuang Zhu
    Department of Laboratory Medicine, Guangdong Provincial Key Laboratory of Precision Medical Diagnostics, Guangdong Engineering and Technology Research Center for Rapid Diagnostic Biosensors, Guangdong Provincial Key Laboratory of Single-Cell and Extracellular Vesicles, Nanfang Hospital, Southern Medical University, Guangzhou, China, zhushuang101@126.com.
  • Bei Luo
    Department of Renal Pathology, King Medical Diagnostics Center, Guangzhou, China.
  • Sendong Lai
    Department of Renal Pathology, King Medical Diagnostics Center, Guangzhou, China.
  • Shuling Yue
    Department of Renal Pathology, King Medical Diagnostics Center, Guangzhou, China.
  • Guang Yang
    National Heart and Lung Institute, Imperial College London, London, UK.
  • Zhen Song
    School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510641, China.
  • Xiaomeng Xu
    Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; NIDA National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA.
  • Yangyang Gui
    Department of Renal Pathology, King Medical Diagnostics Center, Guangzhou, China.
  • Anlan Chen
    Department of Renal Pathology, King Medical Diagnostics Center, Guangzhou, China.
  • Hongmei Yu
    Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
  • Yanqiu Liu
    College of Information Engineering, China Jiliang University, Hangzhou 310018, China.
  • Hongyu Liu
    Department of Physics, Shanghai University of Electric Power, Shanghai 200090, China.
  • Chao Yang
    Translational Institute for Cancer Pain, Chongming Hospital Affiliated to Shanghai University of Health & Medicine Sciences (Xinhua Hospital Chongming Branch), Shanghai 202155, P. R. China.
  • Lei Zheng
    Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Huanhuxi Road, Hexi District, Tianjin 300060, China.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals