Robert M Geraghty, Anshul Thakur, Sarah Howles, William Finch, Sarah Fowler, Alistair Rogers, Seshadri Sriprasad, Daron Smith, Andrew Dickinson, Zara Gall, Bhaskar K Somani
BACKGROUND AND OBJECTIVE: Machine learning (ML) is a subset of artificial intelligence that uses data to build algorithms to predict specific outcomes. Few ML studies have examined percutaneous nephrolithotomy (PCNL) outcomes. Our objective was to build, streamline, temporally validate, and use ML models for prediction of PCNL outcomes (intensive care admission, postoperative infection, transfusion, adjuvant treatment, postoperative complications, visceral injury, and stone-free status at follow-up) using a comprehensive national database (British Association of Urological Surgeons PCNL)...
February 1, 2024: European Urology Focus