Clinical Research Directory

Radiomics and Image Segmentation of Urinary Stones by Artificial Intelligence

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.