xml: <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/NCT06037057</url>
    </required_header>
    <id_info>
      <org_study_id>LY2023-121-B</org_study_id>
      <nct_id>NCT06037057</nct_id>
    </id_info>
    <brief_title>Application of Artificial Intelligence Deep Learning Technology in Magnetic Resonance Lumbar Imaging</brief_title>
    <official_title>Application of Artificial Intelligence Deep Learning Technology in Magnetic Resonance Lumbar Imaging</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>RenJi Hospital</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>RenJi Hospital</source>
    <oversight_info>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>No</is_fda_regulated_device>
    </oversight_info>
    <brief_summary>
      <textblock>
        To study the comparative analysis of artificial intelligence deep learning technology in the&#xD;
        image quality of under-artificial intelligence (AI) reconstruction images and the original&#xD;
        acquisition images of magnetic resonance lumbar spine&#xD;
      </textblock>
    </brief_summary>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">October 8, 2023</start_date>
    <completion_date type="Anticipated">March 8, 2024</completion_date>
    <primary_completion_date type="Anticipated">March 8, 2024</primary_completion_date>
    <study_type>Observational [Patient Registry]</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Cohort</observational_model>
      <time_perspective>Prospective</time_perspective>
    </study_design_info>
    <target_duration>3 Months</target_duration>
    <primary_outcome>
      <measure>Application of Artificial Intelligence Deep Learning Technology in Magnetic Resonance Lumbar Imaging</measure>
      <time_frame>2025.3-2025.6</time_frame>
      <description>manuscript</description>
    </primary_outcome>
    <enrollment type="Anticipated">40</enrollment>
    <condition>Deep Learning,Lumbar Magnetic Resonance Imaging</condition>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>no intervention name</intervention_name>
      <description>no intervention description</description>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          Patients of both sexes, aged ≥18 years, with no history of lumbar surgery, underwent lumbar&#xD;
          magnetic resonance imaging examination.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Patients of both sexes, aged ≥18 years, with no history of lumbar surgery, underwent&#xD;
               lumbar magnetic resonance imaging examination.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  With metal implants in the body, claustrophobic, unable to lie flat for 15 minutes,&#xD;
               with a history of lumbar surgery.&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
    </eligibility>
    <verification_date>September 2023</verification_date>
    <study_first_submitted>September 7, 2023</study_first_submitted>
    <study_first_submitted_qc>September 7, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">September 14, 2023</study_first_posted>
    <last_update_submitted>September 7, 2023</last_update_submitted>
    <last_update_submitted_qc>September 7, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 14, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: 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/NCT06037057
  org study id: LY2023-121-B
  nct id: NCT06037057
  lead sponsor: 
  is fda regulated drug: No
  is fda regulated device: No
  To study the comparative analysis of artificial intelligence deep learning technology in the
        image quality of under-artificial intelligence (AI) reconstruction images and the original
        acquisition images of magnetic resonance lumbar spine
  observational model: Cohort
  time perspective: Prospective
  measure: Application of Artificial Intelligence Deep Learning Technology in Magnetic Resonance Lumbar Imaging
  time frame: 2025.3-2025.6
  description: manuscript
  intervention type: Other
  intervention name: no intervention name
  description: no intervention description
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: Accepts Healthy Volunteers
  responsible party type: Sponsor