Novel noninvasive assessment of upper urinary tract urine flow dynamics: a deep learning-driven reconstruction model combined with CFD simulation.

purposeRecent advances in deep learning have facilitated the exploration of mesh reconstruction techniques; however, these approaches have not yet been applied to mesh reconstruction of hydronephrosis lesions in pediatric patients. Three surgical strategies are currently available for robot-assisted pyeloplasty in children, but there remains no consensus on which yields superior clinical benefits. MATERIALS AND

methodsA total of 36 patients received robot-assisted pyeloplasty were divided into three groups based on different surgical procedures: large anastomosis group (LAG), middle anastomosis group (MAG) and small anastomosis group (SAG).A deep learning network was utilized to reconstruct upper urinary tract models from patients' magnetic resonance urography (MRU) images, followed by computational fluid dynamics (CFD) simulations to measure urine flow dynamics parameters in the reconstructed models. Statistical comparisons of parameters were performed among the three groups.

resultsStatistically significant differences were observed among the three groups in terms of improvement in differential renal function (DRF), reduction in postoperative anteroposterior diameter (APD) of the renal pelvis, postoperative pressure reduction, pressure gradient between the upper and lower planes, changes in urinary flow velocity, and alterations in wall eddy viscosity. DRF was significantly improved in both the MAG and LAG, whereas it was significantly decreased in the SAG. The MAG exhibited the most remarkable reduction in the anteroposterior diameter of the renal pelvis. Postoperative pressure reduction was more pronounced in the MAG when compared with the LAG and SAG. The median postoperative pressure gradient between the upper and lower planes was the highest in the LAG. Postoperative urinary flow velocity increased significantly in the LAG, only slightly in the MAG, but decreased in the SAG. Postoperative wall eddy viscosity increased significantly in the LAG, whereas it decreased in both the MAG and SAG.

conclusionsDeep learning-based upper urinary tract model reconstruction combined with CFD simulation constitutes a novel noninvasive method for measuring upper urinary tract urine flow dynamics parameters. Among the three robot-assisted pyeloplasty techniques, the middle anastomosis approach yields the optimal postoperative recovery.