Machine Learning-Assisted Preoperative Diagnosis of Infection Stones in Urolithiasis Patients

The decision-making of how to treat urinary infection stones was complicated by the difficulty in preoperative diagnosis of these stones. Hence, we developed machine learning (ML) models that can be leveraged to discriminate between infection and noninfection stones in urolithiasis patients before treatment. We enrolled 462 patients with urinary stones and randomly stratified them into training (80%) and testing sets (20%). ML models were constructed using five algorithms (decision tree, random forest classifier [RFC], extreme gradient boosting, categorical boosting, and adaptive boosting) and 15 preoperative variables and were compared with conventional logistic regression (LR) analysis. Performance measurement was the area under the receiver operating characteristic curve (AUC) in the testing set. We also analyzed the importance of 15 features on the prediction of infection stones in each ML model. Sixty-two (13.4%) patients with infection stones were included in the study. On the testing set, all the five ML models demonstrated strong discrimination (AUC: 0.892-0.951). The RFC model was chosen as the final model [AUC: 0.951 (95% confidence interval, CI, 0.934-0.968); sensitivity: 0.906; specificity: 0.924], significantly outperforming the traditional LR model [AUC: 0.873 (95% CI 0.843-0.904)]. Gender, urine white blood cell counts, and urine pH level were the top 3 important features. Our RFC model was the first model for the preoperative identification of infection stones with superior predictive performance. This novel model could be useful for risk assessment and decision support for infection stones.