Detection of Urinary Stones on ULDCT With Deep-learning Image Reconstruction Algorithm (URO DLIR)
Detection of Urinary Tract Stones on Ultra-low Dose Abdominopelvic CT Imaging With Deep-learning Image Reconstruction Algorithm
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Anticipated)
Enrollment
Phase
Phase
- Not Applicable
Contacts and Locations
Study Contact
Study Contact
- Name: Cédric Renard, MD
- Phone Number: (33)322087537
- Email: renard.cedric@chu-amiens.fr
Study Locations
-
-
-
Amiens, France, 80480
- Recruiting
- CHU Amiens
-
Contact:
- Cédric Renard, MD
- Phone Number: (33)322087537
- Email: renard.cedric@chu-amiens.fr
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Description
Inclusion Criteria:
- Age ≥ 18 years old,
- Patient referred for abdominopelvic CT to confirm urolithiasis or for follow-up,
- Affiliation to a social security program,
- Ability of the subject to understand and express opposition
Exclusion Criteria:
- Age <18 years old,
- Person under guardianship or curators,
- Pregnant woman,
- Any contraindications to CT
Study Plan
How is the study designed?
Design Details
- Primary Purpose: DIAGNOSTIC
- Allocation: NA
- Interventional Model: SINGLE_GROUP
- Masking: NONE
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Accuracy between low dose CT using DLIR reconstruction and low dose CT without DLIR reconstruction for the detection of urinary tract stones
Time Frame: day 1
|
Accuracy between low dose CT using DLIR reconstruction and low dose CT without DLIR reconstruction for the detection of urinary tract stones. Patients who were referred to the department for abdominopelvic CT exam for urolithiasis diagnostic or follow-up, and had consented to participate in the study, will undergo an additional ultra-low dose acquisition (ULD, <1 mSv) with deep learning-based reconstruction (DLIR). |
day 1
|
Collaborators and Investigators
Sponsor
Sponsor
Study record dates
Study Major Dates
Study Start (ACTUAL)
Study Start
Primary Completion (ANTICIPATED)
Primary Completion
Study Completion (ANTICIPATED)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (ACTUAL)
First Posted
Study Record Updates
Last Update Posted (ACTUAL)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- PI2020_843_0053
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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