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/NCT05967546</url>
    </required_header>
    <id_info>
      <org_study_id>XJTU1AF2023LSK-170</org_study_id>
      <nct_id>NCT05967546</nct_id>
    </id_info>
    <brief_title>Deep Learning for Intelligent Identification of Arrhythmias</brief_title>
    <acronym>ECG-LEARNING</acronym>
    <official_title>Deep Learning for Intelligent Identification of Arrhythmias (ECG-LEARNING): an Investigator-initiated, National Multicenter, Retrospective-prospective, Cohort Study</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>First Affiliated Hospital Xi'an Jiaotong University</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>521 Hospital of NORINCO Group</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Shaanxi Provincial People's Hospital</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Xiangyang Central Hospital</agency>
        <agency_class>Other</agency_class>
      </collaborator>
    </sponsors>
    <source>First Affiliated Hospital Xi'an Jiaotong University</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>
        This study aims to design and train a deep learning model for the diagnosis of different&#xD;
        arrhythmias.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        This study aims to retrospectively and prospectively collect routine clinical data such as&#xD;
        electrocardiograms from patients with arrhythmias who meet the inclusion and exclusion&#xD;
        criteria. Then we will design and train a deep learning model to analyse the&#xD;
        electrocardiographic features of the arrhythmias, and identify the types of arrhythmias and&#xD;
        evaluate the value of the model for the diagnosis of different arrhythmias.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">October 30, 2023</start_date>
    <completion_date type="Anticipated">December 31, 2028</completion_date>
    <primary_completion_date type="Anticipated">August 31, 2028</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Cohort</observational_model>
      <time_perspective>Other</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>A deep learning model designed to intelligently identify the types of arrhythmia.</measure>
      <time_frame>1 day after the enrollment.</time_frame>
      <description>Deep learning was used to develop diagnostic models and intelligently identify arrhythmia types.</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>The sensitivity, specificity and accuracy of the deep learning model</measure>
      <time_frame>1 day after the enrollment.</time_frame>
      <description>The sensitivity, specificity and accuracy of a deep learning model designed were evaluated by intracardiac electrophysiological examination results to identify patients with arrhythmia from various centers.</description>
    </secondary_outcome>
    <number_of_groups>1</number_of_groups>
    <enrollment type="Anticipated">4000</enrollment>
    <condition>Arrhythmia</condition>
    <arm_group>
      <arm_group_label>Experimental Group</arm_group_label>
      <description>ECG data and clinical data from this group of arrhythmia patients will be used to build a deep learning model.</description>
    </arm_group>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>Observational</intervention_name>
      <description>No interventions will be given to patients.</description>
      <arm_group_label>Experimental Group</arm_group_label>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          Patients diagnosed with arrhythmia by twelve-lead electrocardiogram or Holter.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  For retrospective study: 1.Patients with arrhythmia diagnosed by routine surface&#xD;
               12-lead electrocardiogram or Holter; 2.The type of arrhythmia is diagnosed by&#xD;
               intracardiac electrophysiological examination.&#xD;
  &#xD;
            -  For prospective study: 1.Patients with arrhythmia diagnosed by routine surface 12-lead&#xD;
               electrocardiogram or Holter; 2.Intracardiac electrophysiological examination is&#xD;
               planned.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Lack of routine surface 12-lead electrocardiogram or holter data;&#xD;
  &#xD;
            -  Lack of intracardiac electrophysiological examination;&#xD;
  &#xD;
            -  Patients refused to sign informed consent and refused to participate in the study.&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>3 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Guoliang Li, M.D.</last_name>
      <role>Principal Investigator</role>
      <affiliation>First Affiliated Hospital Xi'an Jiaotong University</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Guoliang Li, M.D.</last_name>
      <phone>+8613759982523</phone>
      <email>liguoliang_med@163.com</email>
    </overall_contact>
    <overall_contact_backup>
      <last_name>Chaofeng Sun, M.D.</last_name>
      <email>cfsun1@mail.xjtu.edu.cn</email>
    </overall_contact_backup>
    <location>
      <facility>
        <name>First Affiliated Hospital of Xi'an Jiantong University</name>
        <address>
          <city>Xi'an</city>
          <state>Shaanxi</state>
          <zip>710061</zip>
          <country>China</country>
        </address>
      </facility>
    </location>
    <location_countries>
      <country>China</country>
    </location_countries>
    <verification_date>June 2023</verification_date>
    <study_first_submitted>July 6, 2023</study_first_submitted>
    <study_first_submitted_qc>July 21, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 1, 2023</study_first_posted>
    <last_update_submitted>July 21, 2023</last_update_submitted>
    <last_update_submitted_qc>July 21, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 1, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <keyword>Arrhythmia</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Arrhythmias, Cardiac</mesh_term>
    </condition_browse>
    <!-- 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/NCT05967546
  org study id: XJTU1AF2023LSK-170
  nct id: NCT05967546
  lead sponsor: 
  collaborator: 
  collaborator: 
  collaborator: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  This study aims to design and train a deep learning model for the diagnosis of different
        arrhythmias.
  This study aims to retrospectively and prospectively collect routine clinical data such as
        electrocardiograms from patients with arrhythmias who meet the inclusion and exclusion
        criteria. Then we will design and train a deep learning model to analyse the
        electrocardiographic features of the arrhythmias, and identify the types of arrhythmias and
        evaluate the value of the model for the diagnosis of different arrhythmias.
  observational model: Cohort
  time perspective: Other
  measure: A deep learning model designed to intelligently identify the types of arrhythmia.
  time frame: 1 day after the enrollment.
  description: Deep learning was used to develop diagnostic models and intelligently identify arrhythmia types.
  measure: The sensitivity, specificity and accuracy of the deep learning model
  time frame: 1 day after the enrollment.
  description: The sensitivity, specificity and accuracy of a deep learning model designed were evaluated by intracardiac electrophysiological examination results to identify patients with arrhythmia from various centers.
  arm group label: Experimental Group
  description: ECG data and clinical data from this group of arrhythmia patients will be used to build a deep learning model.
  intervention type: Other
  intervention name: Observational
  description: No interventions will be given to patients.
  arm group label: Experimental Group
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 3 Years
  maximum age: N/A
  healthy volunteers: No
  last name: Guoliang Li, M.D.
  role: Principal Investigator
  affiliation: First Affiliated Hospital Xi'an Jiaotong University
  last name: Guoliang Li, M.D.
  phone: +8613759982523
  email: liguoliang_med@163.com
  last name: Chaofeng Sun, M.D.
  email: cfsun1@mail.xjtu.edu.cn
  facility: 
  country: China
  responsible party type: Sponsor
  mesh term: Arrhythmias, Cardiac