Urinary StoneRISUS_AINCT06412900
Radiomics and Image Segmentation of Urinary Stones by Artificial Intelligence
An observational study of Urinary Stone and Renal Colic, run by Oslo University Hospital at 1 site in Norway.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Urinary Stone, Renal Colic, Obstruction Ureter
- How many people
- 522 participants
- Where
- 1 siteNorway
- Study dates
- 21 May 2024to 28 Mar 2028
- How long will I take part?
- 3 Months
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients referred for CT for new/acute episode of renal colic and suspicion of /or known urinary stone disease.
Who may be able to join
- 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
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
About this study
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:
Read more
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.
Read the detailed description
Background:
Goals and Objectives:
The project aims to contribute to personalized and improved treatment and follow-up of patients with kidney stones using radiomics and the development of an artificial intelligence tool for CT examination assessment. The objectives are to assess:
- Whether manual segmentation of CT images of the urinary tract provides equivalent or more accurate information about kidney stone disease compared to conventional interpretation and reporting.
- Whether segmentation performed with AI yields valid results compared to manual segmentation.
- Whether AI can detect ureteral stones and obstruction and/or predict spontaneous passage of stones.
Method:
Cohort:
Patients are recruited to the study at Oslo University Hospital, Radiology Department, Section Aker, which performs approximately 1350 CT examinations for urinary tract stones in approximately 1000 patients each year. Approximately 500 patients with a new episode or newly occurring colic pain and clinical suspicion of kidney stones are expected to be included.
Clinical data (where available):
- Baseline CT: date and image data
- Initial treatment (conservative, URS, PCN, ESWL) decision after baseline CT
- Follow-up CT: date and image data
- Time to spontaneous stone passage (negative control CT) or completed surgical intervention (URS)
- Any other surgical/invasive procedure
- Stone chemical analysis
- Clinical biochemistry: creatinine/eGFR, CRP, leukocytes (at baseline and follow-ups).
Image data:
Clinical radiology report:
- Stone: (largest calculus and any obstructing calculus): largest diameter in any plane, density (ROI set by clinical judgment, largest possible ROI - in the slice where the stone is largest), location (upper ureter: above crossing of vessels, lower ureter: below crossing of vessels, ostial: in bladder wall)
- Renal pelvis: largest diameter of calyx neck lower calyx, clinical assessment of dilation (not dilated/slight/moderate/severe).
- Segmentation:
- Stone: total segmented stone volume, largest diameter, and density of segmented stone.
- Collecting system: total segmented volume of the collecting system and renal pelvis.
- Conditions
- Urinary Stone, Renal Colic, Obstruction Ureter, Urosepsis, Urolithiasis, Ureter Stone
- Keywords
- Urolithiasis, Computed tomography, Image segmentation, Ureteral obstruction, Artificial intelligence
- Medical subject headings
- Urinary Calculi, Renal Colic, Ureteral Obstruction, Urolithiasis, Ureterolithiasis, Kidney Calculi
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Adults investigated with CT for suspected urinary stone disease | Newly occurring colic pain and clinical suspicion of kidney stones or known kidney stone with new/increasing symptoms. Age ≥ 18 years |
What is being measured
The primary outcome is the main question the study is designed to answer.
Comparison of stone diameter from manual segmentation with radiology report
Stone diameter (in mm) compared between manual segmentation and radiology report (paired t-test or wilcoxon rank sum test if non-normally distributed data)
Measured: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Comparison of AI-segmentation of stones (DICE-score) with manual segmentation
DICE-score for AI-segmentation of stones, compared to manual segmenation (gold standard)
Measured: 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)
Comparison of differences in dicotomous proportions in paired data according to Newcombe
Measured: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Secondary outcomes (11)
Comparison of stone density from manual segmentation with radiology report
Stone density (in Hounsfield Units) compared between manual segmentation and radiology report (paired t-test or wilcoxon rank sum test if non-normally distributed data)
Measured: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Comparison of distention of renal pelvis from manual segmentation with radiology report
Distention of renal pelvis (in mm) compared between manual segmentation and radiology report (paired t-test or wilcoxon rank sum test if non-normally distributed data)
Measured: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Comparison of AI-segmentation of stones (Hausdorff distance) with manual segmentation
Haussdorff distance for AI-segmentation of stones, compared to manual segmenation (gold standard)
Measured: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Comparison of AI-segmentation of stones (diagnostic accuracy) with manual segmentation
Diagnostic accuracy for AI-segmentation of stones compared to manual segmenation (gold standard)
Measured: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Comparison of AI-segmentation of renal pelvis (Dice-score) with manual segmentation
DICE-score for AI-segmentation of renal pelvis compared to manual segmenation (gold standard)
Measured: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Comparison of AI-segmentation of renal pelvis (Hausdorff distance) with manual segmentation
Hausdorff distance for AI-segmentation of renal pelvis compared to manual segmenation (gold standard)
Measured: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Comparison of AI-segmentation of renal pelvis (diagnostic accuracy) with manual segmentation
Diagnostic accuracy for AI-segmentation of renal pelvis compared to manual segmenation (gold standard)
Measured: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Comparison of AI-segmentation of renal parenchyma (DICE-score) with manual segmentation
DICE-score for AI-segmentation of renal parenchyma compared to manual segmenation (gold standard)
Measured: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Comparison of AI-segmentation of renal parenchyma (Hausdorff distance) with manual segmentation
Hausdorff distance for AI-segmentation of renal parenchyma compared to manual segmenation (gold standard)
Measured: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Comparison of AI-segmentation of renal parenchyma (diagnostic accuracy) with manual segmentation
Diagnostic accuracy for AI-segmentation of renal parenchyma compared to manual segmenation (gold standard)
Measured: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Prospective performance (diagnostic accuracy) of AI detection of ureteral obstruction (compared to radiology report (gold standard)
Comparison of differences in dicotomous proportions in paired data according to Newcombe
Measured: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 May 2024
- Study start21 May 2024
- Primary completion2 Aug 2025 (estimated)
- Study completion28 Mar 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Urinary StoneStudies of the same condition, open and under way.
- More trials run by Oslo University HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 522 participants (actual)
- Target duration
- 3 Months
Sponsor and investigators
- Lead sponsor
- Oslo University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Peter Mæhre Lauritzen, Oslo University Hospital
- Investigators
- Peter M. Lauritzen, MD, PhD, Principal investigator, Oslo University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06412900
- Sponsor's protocol ID
- 31347039
- Other IDs
- 660399
- Acronym
- RISUS_AI
- Status last verified
- Aug 2025
- First submitted
- 30 Apr 2024
- Last updated
- 11 Aug 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 11 Aug 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Radiomics and Image Segmentation of Urinary Stones by Artificial Intelligence
- ClinicalTrials.gov ID
- NCT06412900
- Acronym / sponsor ID
- RISUS_AI / 31347039
- Conditions
- Urinary Stone, Renal Colic, Obstruction Ureter, Urosepsis, Urolithiasis, Ureter Stone
- Keywords
- Urolithiasis, Computed tomography, Image segmentation, Ureteral obstruction, Artificial intelligence
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 522 participants (actual)
- Lead sponsor
- Oslo University HospitalUniversity, hospital or other
- Study start
- 21 May 2024 (actual)
- Primary completion
- 2 Aug 2025 (estimated)
- Study completion
- 28 Mar 2028 (estimated)
- First posted
- 14 May 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Background:
Goals and Objectives:
The project aims to contribute to personalized and improved treatment and follow-up of patients with kidney stones using radiomics and the development of an artificial intelligence tool for CT examination assessment. The objectives are to assess:
- Whether manual segmentation of CT images of the urinary tract provides equivalent or more accurate information about kidney stone disease compared to conventional interpretation and reporting.
- Whether segmentation performed with AI yields valid results compared to manual segmentation.
- Whether AI can detect ureteral stones and obstruction and/or predict spontaneous passage of stones.
Method:
Cohort:
Patients are recruited to the study at Oslo University Hospital, Radiology Department, Section Aker, which performs approximately 1350 CT examinations for urinary tract stones in approximately 1000 patients each year. Approximately 500 patients with a new episode or newly occurring colic pain and clinical suspicion of kidney stones are expected to be included.
Clinical data (where available):
- Baseline CT: date and image data
- Initial treatment (conservative, URS, PCN, ESWL) decision after baseline CT
- Follow-up CT: date and image data
- Time to spontaneous stone passage (negative control CT) or completed surgical intervention (URS)
- Any other surgical/invasive procedure
- Stone chemical analysis
- Clinical biochemistry: creatinine/eGFR, CRP, leukocytes (at baseline and follow-ups).
Image data:
Clinical radiology report:
- Stone: (largest calculus and any obstructing calculus): largest diameter in any plane, density (ROI set by clinical judgment, largest possible ROI - in the slice where the stone is largest), location (upper ureter: above crossing of vessels, lower ureter: below crossing of vessels, ostial: in bladder wall)
- Renal pelvis: largest diameter of calyx neck lower calyx, clinical assessment of dilation (not dilated/slight/moderate/severe).
- Segmentation:
- Stone: total segmented stone volume, largest diameter, and density of segmented stone.
- Collecting system: total segmented volume of the collecting system and renal pelvis.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 3 Months
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Adults investigated with CT for suspected urinary stone disease | – | –Newly occurring colic pain and clinical suspicion of kidney stones or known kidney stone with new/increasing symptoms. Age ≥ 18 years |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients referred for CT for new/acute episode of renal colic and suspicion of /or known urinary stone disease.
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Comparison of stone diameter from manual segmentation with radiology report
Time frame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Stone diameter (in mm) compared between manual segmentation and radiology report (paired t-test or wilcoxon rank sum test if non-normally distributed data)
Comparison of AI-segmentation of stones (DICE-score) with manual segmentation
Time frame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
DICE-score for AI-segmentation of stones, compared to manual segmenation (gold standard)
Prospective performance (diagnostic accuracy) of AI detection of ureteral stone (compared to radiology report (gold standard)
Time frame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Comparison of differences in dicotomous proportions in paired data according to Newcombe
11 secondary outcomes
Comparison of stone density from manual segmentation with radiology report
Time frame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Comparison of distention of renal pelvis from manual segmentation with radiology report
Time frame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Comparison of AI-segmentation of stones (Hausdorff distance) with manual segmentation
Time frame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Comparison of AI-segmentation of stones (diagnostic accuracy) with manual segmentation
Time frame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Comparison of AI-segmentation of renal pelvis (Dice-score) with manual segmentation
Time frame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Comparison of AI-segmentation of renal pelvis (Hausdorff distance) with manual segmentation
Time frame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Comparison of AI-segmentation of renal pelvis (diagnostic accuracy) with manual segmentation
Time frame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Comparison of AI-segmentation of renal parenchyma (DICE-score) with manual segmentation
Time frame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Comparison of AI-segmentation of renal parenchyma (Hausdorff distance) with manual segmentation
Time frame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Comparison of AI-segmentation of renal parenchyma (diagnostic accuracy) with manual segmentation
Time frame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Prospective performance (diagnostic accuracy) of AI detection of ureteral obstruction (compared to radiology report (gold standard)
Time frame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 Aug 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Peter M. Lauritzen, MD, PhD principal investigator
Oslo University Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Urinary Stone and Renal Colic, run by Oslo University Hospital at 1 site in Norway.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 11 Aug 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Norway. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Comparison of stone diameter from manual segmentation with radiology report (time frame: At time of CT examination (inclusion and follow up - expected average 12 weeks)). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 11 Aug 2025.