Interpretable Deep-learning Model Based on Superb Microvascular Imaging for Noninvasive Diagnosis of Interstitial Fibrosis in Chronic Kidney Disease.

Journal: Academic radiology
PMID: 39690075

Abstract

RATIONALE AND OBJECTIVES: To develop an interpretable deep learning (XDL) model based on superb microvascular imaging (SMI) for the noninvasive diagnosis of the degree of interstitial fibrosis (IF) in chronic kidney disease (CKD).

Authors

  • Xiachuan Qin
    Department of Ultrasound, The Second Clinical Medical College, North Sichuan Medical College, Nan Chong, China.
  • Xiaoling Liu
    Department of Endocrinology, Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, China.
  • Weihan Xiao
    Department of Ultrasound, The Second Clinical Medical College, North Sichuan Medical College, Nan Chong, China.
  • Qi Luo
    B-DAT & CICAEET, School of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, PR China.
  • Linlin Xia
    Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China.
  • Chaoxue Zhang
    Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China.

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