Discussion
infection, infection stones, urolithiasis, non-infection stones
RECURRENCES, UROLITHIASIS, RECURRENCE, INFECTION, URINARY TRACT INFECTION
In this study, we explored the applicability of machine learning methods to distinguish infection stones from non-infection stones preoperatively in patients. Among the five machine learning models, the AdaBoost model had the highest AUC. Due to the complexity of infection stones, clinical models integrating conventional parameters may be more effective predictors than considering any parameter alone. One possible way to achieve this is to utilize advanced machine-learning methods that have been applied to the prevention and management of infection stones. The construction of the prediction model is derived from common clinical parameters, which are simple, easy to perform, and do not require high technical requirements. It is suitable for promotion in primary hospitals, thus expanding the application prospect of this study.With the progress and development of minimally invasive surgical techniques and endoscopic instruments, traditional open surgery has been gradually replaced by a variety of minimally invasive surgical methods. The determination of stone types can guide the clinical selection of appropriate treatment methods, and provide a basis for the etiological analysis and the formulation of reasonable surgical plans [The formation of infection stones is closely related to urease-producing bacteria. In the present study, positive preoperative urine culture was a predictor of infection stones [Meanwhile, our study also found that for patients with recurrences more than once, the composition of recurrent stones was not completely consistent, and the incidence of infection stones increased with recurrence. It is very important to remove the stones thoroughly during the operation, antibiotics should be used in the perioperative period, and the corresponding dietary structure should be adjusted according to the composition of the stones after the operation. The treatment of infection stones, a special subset of urolithiasis formed by urinary tract infection, is particularly challenging [Our study has some limitations. First, this study was a single-institution retrospective study with a limited number of cases and some selection bias, and the lack of multicenter external validation limits the satisfactory generalizability of the model to other cohorts. At present, the prediction performance is not accurate enough, and other urine indicators, such as urine microorganisms and imaging features, need to be further added to improve the prediction performance. Further work should include optimization and external validation of the model in a larger cohort from multiple centers.