Deep learning radiomics based on ultrasound images for the assisted diagnosis of chronic kidney disease.

Journal: Nephrology (Carlton, Vic.)
PMID: 39134509

Abstract

AIM: This study aimed to explore the value of ultrasound (US) images in chronic kidney disease (CKD) screening by constructing a CKD screening model based on grey-scale US images.

Authors

  • Shuyuan Tian
    Department of Ultrasound, Tongde Hospital of Zhejiang Province, Hangzhou, 310012, China.
  • Yonghong Yu
    College of Tongda, Nanjing University of Posts and Telecommunication, Yangzhou 225127, China.
  • Kangjian Shi
    College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, PR China.
  • Yunwen Jiang
    Department of Ultrasound, Tongde Hospital of Zhejiang Province, Hangzhou, PR China.
  • Huachun Song
    Department of Ultrasound, Tongde Hospital of Zhejiang Province, Hangzhou, PR China.
  • Yuting Wang
    Respiratory Department, Dongzhimen Hospital Affiliated to BUCM, Beijing, China.
  • Xiaoqian Yan
    Department of Nephropathy, Tongde Hospital of Zhejiang Province, Hangzhou, PR China.
  • Yu Zhong
    Faculty of Engineering, China University of Geosciences, Wuhan 430074, China; Institute for Natural Disaster Risk Prevention and Emergency Management, China University of Geosciences, Wuhan 430074, China.
  • Guoliang Shao
    Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, China. Electronic address: shaogl@zjcc.org.cn.

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