Artificial Intelligence and Machine Learning for Stone Management.

Journal: The Urologic clinics of North America
Published Date: May 22, 2025

Abstract

Stone disease management is continuously evolving through the introduction of novel tools and technologies. Artificial intelligence and machine learning (ML) promise a new technological frontier for the enhancement of urolithiasis diagnosis, treatment, and prevention. This article focuses on the potential for ML algorithms to improve urolithiasis-directed imaging and enhance outcome prediction for spontaneous stone passage, ureteroscopy, shockwave lithotripsy, and percutaneous nephrolithotomy. We also discuss how ML optimizes stone composition evaluation and urinary abnormality detection. Ultimately, we aim to shed light on how ML-based innovations will help personalize treatment and improve the efficiency of stone disease management.

Authors

  • Adithya Balasubramanian
    From the Departments of Radiology (M.K.F.) and Urology (A.B.), New York-Presbyterian Weill Cornell Medical Center, 525 E 68th St, New York, NY 10065-4870; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY (S.W., H.A.V., H.H., A.S.B.); and Department of Biomedical Imaging, ETH-Zurich, Zurich Switzerland (E.K.).
  • Hriday Bhambhvani
    Department of Urology, Weill Cornell Medical College, Starr Pavilion, 525 East 68th Street 9th Floor, New York, NY 10065, USA; Department of Urology, Columbia Irving Medical Center, 161 Ft. Washington Avenue, 11th Floor, New York, NY 10032, USA.
  • Justin Lee
    Department of Cardiology, Sheffield Teaching Hospitals Sheffield S5 7AU, UK.
  • Ojas Shah
    Department of Urology, Columbia University Irving Medical Center, New York, New York, USA.

Keywords

External Resources

Popular Topics
Recent Journals