Management of urinary stones: state of the art and future perspectives by experts in stone disease

Abstract

Application of Artificial Intelligence are promising for automated identification of ureteral stones on CT imaging, prediction of stone composition and 24-hour urinary risk factors by demographics and clinical parameters, assessment of stone composition by evaluation of endoscopic images and prediction of outcomes of stone treatments. The synergy between urologists, nephrologists, and scientists in basic kidney stone research will enhance the depth and breadth of investigations, leading to a more comprehensive understanding of kidney stone formation.