Machine learning-based prediction of stone-free rate after retrograde intrarenal surgery for lower pole renal stones.

Journal: World journal of urology
Published Date: Jul 12, 2025

Abstract

BACKGROUND: Lower pole renal stones (LPS) present unique challenges for retrograde intrarenal surgery (RIRS) due to unfavorable anatomical features, often resulting in suboptimal stone-free rates (SFR). Recent advancements in machine learning (ML) offer new opportunities to predict surgical outcomes and guide clinical decision-making. This study aimed to develop and validate ML-based models to predict SFR following RIRS for LPS.

Authors

  • Hsiang Ying Lee
    Department of Urology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.
  • Yu-Hung Tung
    Department of Urology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.
  • Jose Carlo Elises
    Department of Urology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea.
  • Yen-Chun Wang
    Department of Urology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.
  • Vineet Gauhar
    Endourology Section, European Association of Urology, Arnhem, The Netherlands.
  • Sung Yong Cho
    Department of Urology, College of Medicine, Seoul National University Hospital, Seoul National University, Gwanak-gu, Seoul, South Korea.

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