Pilot study of machine learning in the task of distinguishing high and low-grade pediatric hydronephrosis on ultrasound.

Journal: Investigative and clinical urology
Published Date: Nov 1, 2023

Abstract

PURPOSE: Hydronephrosis is a common pediatric urological condition, characterized by dilation of the renal collecting system. Accurate identification of the severity of hydronephrosis is crucial in clinical management, as high-grade hydronephrosis can cause significant damage to the kidney. In this pilot study, we demonstrate the feasibility of machine learning in differentiating between high and low-grade hydronephrosis in pediatric patients.

Authors

  • Matthew Sloan
    Department of Surgery, Section of Urology, University of Chicago, Chicago, IL, USA.
  • Hui Li
    Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Hernan A Lescay
    Department of Surgery, Section of Urology, University of Chicago, Chicago, IL, USA.
  • Clark Judge
    The University of Chicago Medicine Comer Children's Hospital, Chicago, IL, USA.
  • Li Lan
    Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
  • Parviz Hajiyev
    Department of Surgery, Section of Urology, The University of Chicago, Comer Children's Hospital, Chicago, IL, USA. Electronic address: phajiyev@uchicago.edu.
  • Maryellen L Giger
    Department of Radiology, University of Chicago, 5841 S Maryland Ave., Chicago, IL, 60637, USA.
  • Mohan S Gundeti
    University of Chicago, Chicago, IL, USA. Electronic address: mgundeti@surgery.bsd.uchicago.

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