Active — Not RecruitingNot Applicable

Radiomics and Image Segmentation of Urinary Stones by Artificial Intelligence

Norway522 participantsStarted 2024-05-21

Plain-language summary

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.

Who can participate

Age range

18 Years

Sex

ALL

See this in plain English?

AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.

Inclusion Criteria: * Referral for CT due to episode of renal colic and clinical suspicion of urinary stone disease or * Referral for CT due to new episode of pain in patient with known urinary stone disease * Age ≥ 18 years Exclusion Criteria: * Referral for control CT of asymptomatic patients with known urinary stone disease * Referral for control CT after treatment * Referral for control CT for spontaneous passage of stone. * Lack of informed consent for any reason.

Questions worth asking your doctor

Bring these to your next appointment. They're a starting point for a shared conversation — not a sign you qualify or a recommendation to enrol.

1Based on my diagnosis and history, is this trial worth exploring for me — or is there a standard treatment we should try first?
2What does this trial's phase tell us about how much is already known about its safety and benefit?
3What would taking part actually involve for me — visits, tests, time, and travel?
4What are the known and possible risks or side effects I should weigh, and how would they be monitored?
5If this trial isn't the right fit, what other options or trials would you suggest I look into?

Generated to help you prepare — always confirm anything about your own eligibility and care with the study team and your doctor.

Questions for the trial coordinator

The trial coordinator is the person who runs the study day to day. These cover the practical side — logistics, costs, and what taking part would actually mean for your life. The study team confirms whether you meet the criteria; these are questions to ask, not a sign you qualify.

1What does taking part actually involve week to week — how many visits, where, and how long does each one take?
2What costs are covered by the study, and what might I have to pay for myself, including travel, parking, or time off work?
3What happens during screening, and what happens if the study team confirms I don't meet the criteria after those tests?
4Who pays for the scans, blood work, and other tests the trial requires — the study, my insurance, or me?
5How will being in the trial affect my regular care, and will my own doctor stay informed and involved?
6Can I leave the trial at any point if I change my mind, and what would happen to my care if I do?

A starting point for the conversation — always confirm anything about your own eligibility, costs, and care with the study team and your doctor.

What they're measuring

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)

Trial details

NCT IDNCT06412900

SponsorOslo University Hospital

Sponsor typeOTHER

Study typeOBSERVATIONAL

Primary completion2025-08-02

View on ClinicalTrials.gov More Urinary Stone trials

Plain-language summary

Who can participate

Age range

18 Years

Sex

ALL

See this in plain English?

AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.

What they're measuring

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)