Predicting narrow ureters before ureteroscopic lithotripsy with a neural network: a retrospective bicenter study.

In some patients, the passage of semi-rigid ureteroscopes up the ureter is impossible due to narrow ureteral lumen. We established a neural network to predict the inability of the ureter to accommodate the semi-rigid ureteroscope and the need for active or passive dilatation using non-contrast computed tomography (CT) images. Data were collected retrospectively from two centers of 1989 eligible patients who underwent ureteroscopic lithotripsy with ureteral stones. Patients were categorized into two groups: control and narrow ureter. The network was designed and trained for predicting a narrow ureter during initial ureteroscopic lithotripsy, which integrated multi-scale features of the ureter. The predictive efficacy of neural networks DenseNet3D, ResNet3D, ResNet3D MC, and TimeSformer was compared. Furthermore, a previous ureteroscopy or a history of double-J stent placement, ureteral wall thickness and Hounsfield unit (HU) density of the ureter under the stone were compared. Model performance was assessed based on the accuracy, area under the receiver operating characteristic curve (AUC ROC), etc. The DenseNet3D-based network achieved an AUC ROC score of 0.884 and an accuracy of 85.29%, followed by the ResNet3D-based network, the ResNet3D MC-based network, and the TimeSformer-based network. The DenseNet3D-based network significantly outperformed other candidate predictors. Furthermore, the networks were validated in an external test set. Decision curve analysis showed the clinical utility of the neural network. The neural network provides an individualized preoperative prediction of narrow ureter based on non-contrast CT images, which could be employed as part of a surgical decision-making support system.