End-to-end interstitial fibrosis assessment of kidney biopsies with a machine learning-based model.

Journal: Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association
PMID: 35512604

Abstract

BACKGROUND: The extent of interstitial fibrosis in the kidney not only correlates with renal function at the time of biopsy but also predicts future renal outcome. However, its assessment by pathologists lacks good agreement. The aim of this study is to construct a machine learning-based model that enables automatic and reliable assessment of interstitial fibrosis in human kidney biopsies.

Authors

  • Zhi-Yong Liu
    Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital Linkou Main Branch, Taoyuan, Taiwan.
  • Chi-Hung Lin
    Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital Linkou Main Branch, Taoyuan, Taiwan.
  • Hsiang-Sheng Wang
    Department of Anatomic Pathology, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
  • Mei-Chin Wen
    Department of Pathology, China Medical University Hsinchu Hospital, Hsinchu, Taiwan.
  • Wei-Chou Lin
    Department of Pathology, National Taiwan University Hospital, Taipei, Taiwan.
  • Shun-Chen Huang
    Department of Anatomic Pathology, Chang Gung Memorial Hospital at Kaohsiung, Kaohsiung, Taiwan.
  • Kun-Hua Tu
    Department of Nephrology, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
  • Chang-Fu Kuo
    Department of Rheumatology, Allergy, and Immunology, Chang Gung Memorial Hospital, Taipei, Taiwan, ROC.
  • Tai-Di Chen
    Department of Anatomic Pathology, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan. Electronic address: b8902028@msn.com.

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