MedTrace Logo

Radiomics and Image Segmentation of Urinary Stones by Artificial Intelligence

NCT06412900 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 522

Last updated 2025-08-11

No results posted yet for this study

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

Sponsors & Collaborators

  • Oslo University Hospital

    lead OTHER

Principal Investigators

  • Peter M. Lauritzen, MD, PhD · Oslo University Hospital

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-05-21
Primary Completion
2025-08-02
Completion
2028-03-28

Countries

  • Norway

Study Locations

More Related Trials

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06412900 on ClinicalTrials.gov