Deep learning-enabled classification of kidney allograft rejection on whole slide histopathologic images.

Journal: Frontiers in immunology
PMID: 39034991

Abstract

BACKGROUND: Diagnosis of kidney transplant rejection currently relies on manual histopathological assessment, which is subjective and susceptible to inter-observer variability, leading to limited reproducibility. We aim to develop a deep learning system for automated assessment of whole-slide images (WSIs) from kidney allograft biopsies to enable detection and subtyping of rejection and to predict the prognosis of rejection.

Authors

  • Yongrong Ye
    Department of Kidney Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Liubing Xia
    Department of Kidney Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Shicong Yang
    Department of Pathology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • You Luo
    Department of Kidney Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Zuofu Tang
    Organ Transplantation Research Institution, Division of Kidney Transplantation, Department of Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
  • Yuanqing Li
    Center for Brain Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, China.
  • Lanqing Han
    Artificial Intelligence Innovation Center, Research Institute of Tsinghua, Pearl River Delta, Guangzhou, China.
  • Hanbin Xie
    Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Yong Ren
    Artificial Intelligence Innovation Center, Research Institute of Tsinghua, Pearl River Delta, Guangzhou, China.
  • Ning Na
    Department of Kidney Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.

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