Predictive value of machine learning model based on CT values for urinary tract infection stones
Jiaxin Li,
Yao Du,
Gaoming Huang,
Chiyu Zhang,
Zhenfeng Ye,
Jinghui Zhong,
Xiaoqing Xi,
Yawei Huang
Affiliations
Jiaxin Li
Department of Urology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, China
Yao Du
Department of Cardiovascular Medicine, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, China
Gaoming Huang
Department of Urology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, China
Chiyu Zhang
Department of Urology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, China
Zhenfeng Ye
Department of Urology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, China
Jinghui Zhong
Department of Neurology, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China
Xiaoqing Xi
Department of Urology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, China; Corresponding author
Yawei Huang
Department of Urology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, China; Corresponding author
Summary: Preoperative diagnosis of infection stones presents a significant clinical challenge. We developed a machine learning model to predict urinary infection stones using computed tomography (CT) values, enabling in vivo preoperative identification. In this study, we included 1209 patients who underwent urinary lithotripsy at our hospital. Seven machine learning algorithms along with eleven preoperative variables were used to construct the prediction model. Subsequently, model performance was evaluated by calculating AUC and AUPR for subjects in the validation set. On the validation set, all seven machine learning models demonstrated strong discrimination (AUC: 0.687–0.947). Additionally, the XGBoost model was identified as the optimal model significantly outperforming the traditional LR model. Taken together, the XGBoost model is the first machine learning model for preoperative prediction of infection stones based on CT values. It can rapidly and accurately identify infection stones in vitro, providing valuable guidance for urologists in managing these stones.
