Development and External Validation of a Multidimensional Deep Learning Model to Dynamically Predict Kidney Outcomes in IgA Nephropathy.

Journal: Clinical journal of the American Society of Nephrology : CJASN
PMID: 38728096

Abstract

KEY POINTS: A dynamic model predicts IgA nephropathy prognosis based on deep learning. Longitudinal clinical data and deep learning improve predictive accuracy and interpretability in GN.

Authors

  • Tingyu Chen
    National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.
  • Tiange Chen
    Ping An Healthcare Technology, Beijing.
  • Wenjie Xu
    Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
  • Shaoshan Liang
    National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China.
  • Feng Xu
    Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China.
  • Dandan Liang
    National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China.
  • Xiang Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
  • Caihong Zeng
    National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.
  • Guotong Xie
    Ping An Health Technology, Beijing, China.
  • Zhihong Liu
    National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.

Keywords

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