A machine learning approach using stone volume to predict stone-free status at ureteroscopy.

Journal: World journal of urology
PMID: 38775943

Abstract

INTRODUCTION: To develop a predictive model incorporating stone volume along with other clinical and radiological factors to predict stone-free (SF) status at ureteroscopy (URS).

Authors

  • Ganesh Vigneswaran
    School of Cancer Sciences, Faculty of Medicine, University of Southampton, UK.
  • Ren Teh
    Department of Interventional Radiology, University Hospital Southampton, Southampton, UK.
  • Francesco Ripa
    Department of Urology, IRCCS Foundation Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy.
  • Amelia Pietropaolo
    Department of Urology, University Hospital Southampton, Tremona Road, Southampton, UK.
  • Sachin Modi
    Department of Interventional Radiology, University Hospital Southampton, Southampton, UK.
  • Jagmohan Chauhan
    Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom.
  • Bhaskar Kumar Somani
    Department of Urology, University Hospital Southampton, Tremona Road, Southampton, UK. bhaskarsomani@yahoo.com.

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