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.
See this in plain English?
AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Comparison of stone diameter from manual segmentation with radiology report
Timeframe: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Comparison of AI-segmentation of stones (DICE-score) with manual segmentation
Timeframe: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Prospective performance (diagnostic accuracy) of AI detection of ureteral stone (compared to radiology report (gold standard)
Timeframe: At time of CT examination (inclusion and follow up - expected average 12 weeks)