View clinical trials related to Urinary Stone.
Filter by:Kidney stone disease causes significant morbidity, and stones obstructing the ureter can have serious consequences. Imaging diagnostics with computed tomography (CT) are crucial for diagnosis, treatment selection, and follow-up. Segmentation of CT images can provide objective data on stone burden and signs of obstruction. Artificial intelligence (AI) can automate such segmentation but can also be used for the diagnosis of stone disease and obstruction. In this project, the aim is to investigate if: Manual segmentation of CT scans can provide more accurate information about kidney stone disease compared to conventional interpretation. AI segmentation yields valid results compared to manual segmentation. AI can detect ureteral stones and obstruction or predict spontaneous passage.
This is a prospective, open-label, multi-center study to test the clinical feasibility of facilitating stone passage by the combination of breaking and repositioning stones with ultrasound, without the need for anesthesia.
The incidence of nephrolithiasis in children has been reported to increase by approximately 6-10% annually, and the incidence is currently 50 per 100,000 children with high recurrent rate. Investigators aimed to determine the metabolic risk factors in Chinese children through metabolic evaluation. In order to identify diagnostic criteria of hypocitraturia and hyperoxaluria in western country wether adapt to Chinese children, investigators aim to determine normal urine levels of oxalate and citrate in children without kidney stone.