Prediction of operation time in percutaneous nephrolithotomy (PCNL) patients: A machine learning approach.

Journal: Urologia
Published Date: Aug 1, 2025

Abstract

PURPOSE: To investigate the factors influencing the length of percutaneous nephrolithotomy (PCNL) procedures and identify predictive variables for operation time using machine learning models.

Authors

  • Owais Ghammaz
    Faculty of Medicine, Jordan University of Science & Technology, Irbid, Jordan.
  • Rami AlAzab
    Department of General Surgery and Urology, University of Science and Technology, Irbid 22110, Jordan.
  • Nabil Ardah
    Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan.
  • Mohammed Jalal Akel
    Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan.
  • Bashar Tayyem
    Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan.
  • Nazih Alhirtani
    Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan.
  • Abdallah Bakeer
    Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan.
  • Bader Al-Deen Anabtawi
    Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan.
  • Eyas Amaierh
    Department of Urology, King Abdullah University Hospital, Jordan University of Science and Technology, Irbid, Jordan.
  • Azhar Al-Alwani
    Department of Urology, King Abdullah University Hospital, Jordan University of Science and Technology, Irbid, Jordan.

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