Radiomics and Image Segmentation of Urinary Stones by Artificial Intelligence

NCT06412900 | Active Not Recruiting

• 2 other identifiers: 31347039660399
• Study Type: observational
• Target: 522
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Urinary Stone; Renal Colic; Obstruction Ureter; Urosepsis; Urolithiasis; Ureter Stone; Kidney Stone

Keywords

Urolithiasis; Computed tomography; Image segmentation; Ureteral obstruction; Artificial intelligence

Outcome Measures

Primary Outcomes (3)

• 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)

  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)

  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

  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

  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

  At time of CT examination (inclusion and follow up - expected average 12 weeks)

• Comparison of AI-segmentation of stones (Hausdorff distance) with manual segmentation

  At time of CT examination (inclusion and follow up - expected average 12 weeks)

• Comparison of AI-segmentation of stones (diagnostic accuracy) with manual segmentation

  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

  At time of CT examination (inclusion and follow up - expected average 12 weeks)

Study Arms (1)

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

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients referred for CT for new/acute episode of renal colic and suspicion of /or known urinary stone disease.

You may qualify if:

• 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

You may not qualify if:

• 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.

Sponsors & Collaborators

• Oslo University Hospital: lead

Study Sites (1)

Oslo University Hospital, Aker

Oslo, 0586, Norway

Location

MeSH Terms

Conditions

Urinary Calculi; Renal Colic; Ureteral Obstruction; Urolithiasis; Ureterolithiasis; Kidney Calculi

Condition Hierarchy (Ancestors)

Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases; Calculi; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms; Pain; Neurologic Manifestations; Signs and Symptoms; Ureteral Diseases; Nephrolithiasis; Kidney Diseases

Study Officials

• Peter M. Lauritzen, MD, PhD

  Oslo University Hospital

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 3 Months
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal investigator

Study Record Dates

• First Submitted: April 30, 2024
• First Posted: May 14, 2024
• Study Start: May 21, 2024
• Primary Completion: August 2, 2025
• Study Completion (Estimated): March 28, 2028
• Last Updated: August 11, 2025

Record last verified: 2025-08

Data Sharing

• IPD Sharing: Will not share

Locations

NO (1)