Artificial intelligence-assisted quantification and assessment of whole slide images for pediatric kidney disease diagnosis.

Journal: Bioinformatics (Oxford, England)
Published Date: Jan 2, 2024

Abstract

MOTIVATION: Pediatric kidney disease is a widespread, progressive condition that severely impacts growth and development of children. Chronic kidney disease is often more insidious in children than in adults, usually requiring a renal biopsy for diagnosis. Biopsy evaluation requires copious examination by trained pathologists, which can be tedious and prone to human error. In this study, we propose an artificial intelligence (AI) method to assist pathologists in accurate segmentation and classification of pediatric kidney structures, named as AI-based Pediatric Kidney Diagnosis (APKD).

Authors

  • Chunyue Feng
    The Children's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
  • Kokhaur Ong
    Institute of Molecular and Cell Biology, A*STAR, Singapore, Singapore.
  • David M Young
    Institute of Molecular and Cell Biology, A*STAR, Singapore 138673, Singapore.
  • Bingxian Chen
    Ningbo Konfoong Bioinformation Tech Co., Ltd, Ningbo, China.
  • Longjie Li
    Institute of Molecular and Cell Biology, A*STAR, Singapore, Singapore.
  • Xinmi Huo
    Bioinformatics Institute, A*STAR, Singapore 138673, Singapore.
  • Haoda Lu
    School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, P.R.China;Jiangsu Key Laboratory of Large Data Analysis Technology, Nanjing 210044, P.R.China.
  • Weizhong Gu
    National Clinical Research Center for Child Health, Hangzhou 310000, China.
  • Fei Liu
    Department of Interventional Radiology, Qinghai Red Cross Hospital, Xining, Qinghai, China.
  • Hongfeng Tang
    National Clinical Research Center for Child Health, Hangzhou 310000, China.
  • Manli Zhao
    National Clinical Research Center for Child Health, Hangzhou 310000, China.
  • Min Yang
    College of Food Science and Engineering, Ocean University of China, Qingdao, 266003, Shandong, China.
  • Kun Zhu
    Aviation Technology Research Institute, China Aerospace Science and Industry Corporation, Beijing, 100143, China.
  • Limin Huang
    College of Sports and Human Sciences, Harbin Sport University, Harbin 150008, China.
  • Qiang Wang
    Ningbo Konfoong Bioinformation Tech Co., Ltd, Ningbo, China.
  • Gabriel Pik Liang Marini
    Bioinformatics Institute, A*STAR, Singapore 138673, Singapore.
  • Kun Gui
    Ningbo Konfoong Bioinformation Tech Co., Ltd, Ningbo, China.
  • Hao Han
    Institute of Molecular and Cell Biology, A*STAR, Singapore, Singapore.
  • Stephan J Sanders
    Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA, 94143, United States.
  • Lin Li
    Department of Medicine III, LMU University Hospital, LMU Munich, Munich, Germany.
  • Weimiao Yu
    Institute of Molecular and Cell Biology, A*STAR, Singapore, Singapore. yu_weimiao@bii.a-star.edu.sg.
  • Jianhua Mao
    The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China.

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