Deep Learning for the Study of Urinary Stone Composition from Computed Tomography Images.

Journal: Archivos espanoles de urologia
PMID: 39632524

Abstract

OBJECTIVES: Urinary stones composed of uric acid can be treated with medicine. Computed tomography (CT) can diagnose urinary stone disease, but it is difficult to predict the type of uric stones. This study aims to develop a method to distinguish pure uric acid (UA) stones from non-uric acid (non-UA) stones by describing quantitative CT parameters of single-energy slices of urinary stones related to chemical stone types.

Authors

  • Yuanchao Cao
    Department of Urology, Affiliated Hospital of Qingdao University, 266000 Qingdao, Shandong, China.
  • Hang Yuan
    Department of Medical Affairs, MSD (China) Co., Ltd., Shanghai, China.
  • Yang Guo
    Innovation Research Institute of Combined Acupuncture and Medicine, Shaanxi University of CM, Xianyang 712046, China.
  • Bin Li
    Department of Magnetic Resonance Imaging (MRI), Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
  • Xinning Wang
    Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Xinsheng Wang
    The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Yanjiang Li
    Department of Urology, Affiliated Hospital of Qingdao University, 266000 Qingdao, Shandong, China.
  • Wei Jiao
    Department of Spinal Surgery, Fuyang City People's Hospital, Fuyang Anhui, 236000, P.R.China.

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