Machine-learning model based on ultrasomics for non-invasive evaluation of fibrosis in IgA nephropathy.

Journal: European radiology
Published Date: Jan 24, 2025

Abstract

OBJECTIVES: To develop and validate an ultrasomics-based machine-learning (ML) model for non-invasive assessment of interstitial fibrosis and tubular atrophy (IF/TA) in patients with IgA nephropathy (IgAN).

Authors

  • Qun Huang
    Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, China.
  • Fangyi Huang
    Department of Ultrasound, First Affiliated Hospital of Guangxi Medical University, Nanning, China.
  • Chengcai Chen
    Department of Ultrasound, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China.
  • Pan Xiao
    Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China.
  • Jiali Liu
    Department of Clinical Oncology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China.
  • Yong Gao
    Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China.

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