Machine Learning Predicts Clinically Significant Kidney Stones

Researchers from three Kaohsiung, Taiwan hospitals developed and validated a machine-learning model for screening clinically significant nephrolithiasis using routine health data collected from 2012–2021, including 6,528 adults. The model used 10 variables and achieved an AUROC of 0.968, AUPRC 0.936, sensitivity 0.873, and specificity 0.947. Shapley analysis flagged urine red blood cell count, eGFR, and urine specific gravity as top predictors, suggesting integration into health checkups or telemedicine.