UroAngel: a single-kidney function prediction system based on computed tomography urography using deep learning.

Journal: World journal of urology
PMID: 38627315

Abstract

BACKGROUND: Accurate estimation of the glomerular filtration rate (GFR) is clinically crucial for determining the status of obstruction, developing treatment strategies, and predicting prognosis in obstructive nephropathy (ON). We aimed to develop a deep learning-based system, named UroAngel, for non-invasive and convenient prediction of single-kidney function level.

Authors

  • Qingyuan Zheng
    Department of Urology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
  • Xinmiao Ni
    Department of Urology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
  • Rui Yang
    Department of Biomedical Informatics, Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore.
  • Panpan Jiao
    Department of Urology, Renmin Hospital of Wuhan University, 99 Zhang Zhi-Dong Road, Wuhan, 430060, Hubei, People's Republic of China.
  • Jiejun Wu
    Department of Urology, Renmin Hospital of Wuhan University, 99 Zhang Zhi-Dong Road, Wuhan, 430060, Hubei, People's Republic of China.
  • Song Yang
    Key Laboratory of Pesticide Toxicology&Application Technique, College of Plant Protection, Shandong Agricultural University, Tai'an 271018, China.
  • Zhiyuan Chen
    Department of Urology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
  • Xiuheng Liu
    Department of Urology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
  • Lei Wang
    Department of Nursing, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.

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