<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT04490343</url>
  </required_header>
  <id_info>
    <org_study_id>PI2020_843_0053</org_study_id>
    <nct_id>NCT04490343</nct_id>
  </id_info>
  <brief_title>Detection of Urinary Stones on ULDCT With Deep-learning Image Reconstruction Algorithm</brief_title>
  <acronym>URO DLIR</acronym>
  <official_title>Detection of Urinary Tract Stones on Ultra-low Dose Abdominopelvic CT Imaging With Deep-learning Image Reconstruction Algorithm</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Centre Hospitalier Universitaire, Amiens</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Centre Hospitalier Universitaire, Amiens</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      Urolithiasis has an increasing incidence and prevalence worldwide, and some patients may have&#xD;
      multiple recurrences. Because these stone-related episodes may lead to multiple diagnostic&#xD;
      examinations requiring ionizing radiation, urolithiasis is a natural target for dose&#xD;
      reduction efforts. Abdominopelvic low dose CT, which has the highest sensitivity and&#xD;
      specificity among available imaging modalities, is the most appropriate diagnostic exam for&#xD;
      this pathology. The main objective of this study is to evaluate the diagnostic performance of&#xD;
      ultra-low dose CT using deep learning-based reconstruction in urolithiasis patients.&#xD;
    </textblock>
  </brief_summary>
  <overall_status>Active, not recruiting</overall_status>
  <start_date type="Actual">July 21, 2020</start_date>
  <completion_date type="Anticipated">July 1, 2023</completion_date>
  <primary_completion_date type="Actual">July 1, 2022</primary_completion_date>
  <phase>N/A</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>N/A</allocation>
    <intervention_model>Single Group Assignment</intervention_model>
    <primary_purpose>Diagnostic</primary_purpose>
    <masking>None (Open Label)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>Accuracy between low dose CT using DLIR reconstruction and low dose CT without DLIR reconstruction for the detection of urinary tract stones</measure>
    <time_frame>day 1</time_frame>
    <description>Accuracy between low dose CT using DLIR reconstruction and low dose CT without DLIR reconstruction for the detection of urinary tract stones.&#xD;
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, &lt;1 mSv) with deep learning-based reconstruction (DLIR).</description>
  </primary_outcome>
  <enrollment type="Actual">62</enrollment>
  <condition>Urolithiasis</condition>
  <condition>Urinary Tract Stones</condition>
  <condition>Renal Colic</condition>
  <condition>Deep Learning Reconstruction</condition>
  <intervention>
    <intervention_type>Diagnostic Test</intervention_type>
    <intervention_name>Abdominopelvic low dose CT</intervention_name>
    <description>Patients with urinary stones will undergo multiple computed tomography (CT) examinations</description>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Age ≥ 18 years old,&#xD;
&#xD;
          -  Patient referred for abdominopelvic CT to confirm urolithiasis or for follow-up,&#xD;
&#xD;
          -  Affiliation to a social security program,&#xD;
&#xD;
          -  Ability of the subject to understand and express opposition&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Age &lt;18 years old,&#xD;
&#xD;
          -  Person under guardianship or curators,&#xD;
&#xD;
          -  Pregnant woman,&#xD;
&#xD;
          -  Any contraindications to CT&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <location>
    <facility>
      <name>CHU Amiens</name>
      <address>
        <city>Amiens</city>
        <zip>80480</zip>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>France</country>
  </location_countries>
  <verification_date>March 2023</verification_date>
  <study_first_submitted>July 24, 2020</study_first_submitted>
  <study_first_submitted_qc>July 24, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">July 29, 2020</study_first_posted>
  <last_update_submitted>March 7, 2023</last_update_submitted>
  <last_update_submitted_qc>March 7, 2023</last_update_submitted_qc>
  <last_update_posted type="Actual">March 8, 2023</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>Urolithiasis</keyword>
  <keyword>Urinary Tract Stones</keyword>
  <keyword>Renal Colic</keyword>
  <keyword>deep learning reconstruction</keyword>
  <keyword>radiation dose</keyword>
  <keyword>dose reduction</keyword>
  <keyword>low dose CT</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Urolithiasis</mesh_term>
    <mesh_term>Urinary Calculi</mesh_term>
    <mesh_term>Calculi</mesh_term>
    <mesh_term>Renal Colic</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT04490343
org study id: PI2020_843_0053
nct id: NCT04490343
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
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.
allocation: N/A
intervention model: Single Group Assignment
primary purpose: Diagnostic
masking: None (Open Label)
measure: 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
description: 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).
intervention type: Diagnostic Test
intervention name: Abdominopelvic low dose CT
description: Patients with urinary stones will undergo multiple computed tomography (CT) examinations
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
facility: 
country: France
responsible party type: Sponsor
mesh term: Urolithiasis
mesh term: Urinary Calculi
mesh term: Calculi
mesh term: Renal Colic
sharing ipd: No