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Detection of Urinary Stones on ULDCT With Deep-learning Image Reconstruction Algorithm

NCT04490343 · Status: UNKNOWN · Phase: NA · Type: INTERVENTIONAL · Enrollment: 62

Last updated 2023-03-08

No results posted yet for this study

Summary

Urolithiasis has an increasing incidence and prevalence worldwide, and some patients may have multiple recurrences. Because these stone-related episodes may lead to multiple diagnostic examinations requiring ionizing radiation, urolithiasis is a natural target for dose reduction efforts. Abdominopelvic low dose CT, which has the highest sensitivity and specificity among available imaging modalities, is the most appropriate diagnostic exam for this pathology. The main objective of this study is to evaluate the diagnostic performance of ultra-low dose CT using deep learning-based reconstruction in urolithiasis patients.

Conditions

  • Urolithiasis
  • Urinary Tract Stones
  • Renal Colic
  • Deep Learning Reconstruction

Interventions

DIAGNOSTIC_TEST

Abdominopelvic low dose CT

Patients with urinary stones will undergo multiple computed tomography (CT) examinations

Sponsors & Collaborators

  • Centre Hospitalier Universitaire, Amiens

    lead OTHER

Study Design

Allocation
NA
Purpose
DIAGNOSTIC
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2020-07-21
Primary Completion
2022-07-01
Completion
2023-07-01

Countries

  • France

Study Locations

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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 NCT04490343 on ClinicalTrials.gov