Severity-stratification of interstitial lung disease by deep learning enabled assessment and quantification of lesion indicators from HRCT images.

Journal: Journal of X-ray science and technology
Published Date: Jan 1, 2024

Abstract

BACKGROUND: Interstitial lung disease (ILD) represents a group of chronic heterogeneous diseases, and current clinical practice in assessment of ILD severity and progression mainly rely on the radiologist-based visual screening, which greatly restricts the accuracy of disease assessment due to the high inter- and intra-subjective observer variability.

Authors

  • Yexin Lai
    College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China.
  • Xueyu Liu
    College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China.
  • Fan Hou
    Institute of Public-Safety and Big Data, College of Data Science, Taiyuan University of Technology, Taiyuan.
  • Zhiyong Han
    College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China.
  • Linning E
    Department of Radiology, People's Hospital of Longhua, Shenzhen, China.
  • Ningling Su
    From the Department of Radiology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University.
  • Dianrong Du
    College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China.
  • Zhichong Wang
    College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China.
  • Wen Zheng
    College of Data Science, Taiyuan University of Technology, Taiyuan, 030024, China.
  • Yongfei Wu
    College of Data Science, Taiyuan University of Technology, Taiyuan, 030024, China. Electronic address: wuyongfei@tyut.edu.cn.

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