Predictive modelling of urinary stone composition using machine learning and clinical data: implications for treatment strategies and pathophysiological insights

Preventative strategies and surgical treatments for urolithiasis depend on stone composition. However, stone composition is often unknown until the stone is passed or surgically managed. Given that stone composition likely reflects the physiological parameters during its formation, we used clinical data from stone formers to predict stone composition.