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/NCT05981950</url>
    </required_header>
    <id_info>
      <org_study_id>Real-world RAIDS</org_study_id>
      <nct_id>NCT05981950</nct_id>
    </id_info>
    <brief_title>Real-world of AI in Diagnosing Retinal Diseases</brief_title>
    <official_title>Real-world Application of Using Artificial Intelligence in Diagnosing Retinal Diseases</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Beijing Tongren Hospital</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Beijing Tongren Hospital</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>
        The objective of this study is to apply an artificial intelligence algorithm to diagnose&#xD;
        multi-retinal diseases in real-world settings. The effectiveness and accuracy of this&#xD;
        algorithm are evaluated by sensitivity, specificity, positive predictive value, negative&#xD;
        predictive value, and area under curve.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        The objective of this study is to apply an artificial intelligence algorithm to diagnose&#xD;
        referral diabetes retinopathy, referral age-related macular degeneration, referral possible&#xD;
        glaucoma, pathological myopia, retinal vein occlusion, macular hole, macular epiretinal&#xD;
        membrane, hypertensive retinopathy, myelinated fibers, retinitis pigmentosa and other retinal&#xD;
        lesions from fundus photography. tic 45-degree fundus cameras, trained operators took&#xD;
        binocular fundus photography on participants. Operators were then asked to identify gradable&#xD;
        images and unload for algorithm diagnosis. The effectiveness and accuracy of this algorithm&#xD;
        are evaluated by sensitivity, specificity, positive predictive value, negative predictive&#xD;
        value, area under curve, and F1 score.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Recruiting</overall_status>
    <start_date type="Actual">August 1, 2023</start_date>
    <completion_date type="Anticipated">August 1, 2029</completion_date>
    <primary_completion_date type="Anticipated">August 1, 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>Prospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Area under curve</measure>
      <time_frame>1 month</time_frame>
      <description>We used the receiver operating characteristic (ROC) curve and area under curve to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Sensitivity and specificity</measure>
      <time_frame>1 month</time_frame>
      <description>We used sensitivity and specificity to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Positive predictive value, negative predictive value</measure>
      <time_frame>1 month</time_frame>
      <description>We used positive predictive value and negative predictive value to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>F1 score</measure>
      <time_frame>1 month</time_frame>
      <description>We used F1 score to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.</description>
    </primary_outcome>
    <number_of_groups>1</number_of_groups>
    <enrollment type="Anticipated">100000</enrollment>
    <condition>Artificial Intelligence</condition>
    <condition>Retinal Diseases</condition>
    <arm_group>
      <arm_group_label>Retinal diseases diagnosed by artificial intelligence algorithm</arm_group_label>
      <description>An artificial intelligence algorithm was applied to diagnose referral diabetes retinopathy, referral age-related macular degeneration, referral possible glaucoma, pathological myopia, retinal vein occlusion, macular hole, macular epiretinal membrane, hypertensive retinopathy, myelinated fibers, retinitis pigmentosa and other retinal lesions from fundus photography.</description>
    </arm_group>
    <intervention>
      <intervention_type>Diagnostic Test</intervention_type>
      <intervention_name>artificial intelligence algorithm</intervention_name>
      <description>Retinal diseases diagnosed by artificial intelligence algorithm</description>
      <arm_group_label>Retinal diseases diagnosed by artificial intelligence algorithm</arm_group_label>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          The study population is derived from an anonymous database that contains health examination&#xD;
          results of the general population.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  fundus photography around 45° field which covers optic disc and macula&#xD;
  &#xD;
            -  complete identification information&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  insufficient information for diagnosis&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>1 Year</minimum_age>
      <maximum_age>100 Years</maximum_age>
    </eligibility>
    <overall_contact>
      <last_name>Wenbin Wei, MD</last_name>
      <phone>58269516</phone>
      <email>weiwenbintr@163.cim</email>
    </overall_contact>
    <overall_contact_backup>
      <last_name>Ruiheng Zhang, MD</last_name>
      <phone>18801121782</phone>
      <email>zhangruihengsy@outlook.com</email>
    </overall_contact_backup>
    <location>
      <facility>
        <name>Wen-Bin Wei</name>
        <address>
          <city>Beijing</city>
          <state>Beijing</state>
          <zip>100730</zip>
          <country>China</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Wen-Bin Wei, MD</last_name>
        <phone>58269516</phone>
        <email>weiwenbintr@163.com</email>
      </contact>
      <investigator>
        <last_name>Wen-Bin Wei, MD</last_name>
        <role>Principal Investigator</role>
      </investigator>
    </location>
    <location_countries>
      <country>China</country>
    </location_countries>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>August 1, 2023</study_first_submitted>
    <study_first_submitted_qc>August 1, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 8, 2023</study_first_posted>
    <last_update_submitted>August 1, 2023</last_update_submitted>
    <last_update_submitted_qc>August 1, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 8, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>Beijing Tongren Hospital</investigator_affiliation>
      <investigator_full_name>Wenbin Wei</investigator_full_name>
      <investigator_title>Prof</investigator_title>
    </responsible_party>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Retinal Diseases</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>No</sharing_ipd>
    </patient_data>
    <!-- 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/NCT05981950
  org study id: Real-world RAIDS
  nct id: NCT05981950
  lead sponsor: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  The objective of this study is to apply an artificial intelligence algorithm to diagnose
        multi-retinal diseases in real-world settings. The effectiveness and accuracy of this
        algorithm are evaluated by sensitivity, specificity, positive predictive value, negative
        predictive value, and area under curve.
  The objective of this study is to apply an artificial intelligence algorithm to diagnose
        referral diabetes retinopathy, referral age-related macular degeneration, referral possible
        glaucoma, pathological myopia, retinal vein occlusion, macular hole, macular epiretinal
        membrane, hypertensive retinopathy, myelinated fibers, retinitis pigmentosa and other retinal
        lesions from fundus photography. tic 45-degree fundus cameras, trained operators took
        binocular fundus photography on participants. Operators were then asked to identify gradable
        images and unload for algorithm diagnosis. The effectiveness and accuracy of this algorithm
        are evaluated by sensitivity, specificity, positive predictive value, negative predictive
        value, area under curve, and F1 score.
  observational model: Cohort
  time perspective: Prospective
  measure: Area under curve
  time frame: 1 month
  description: We used the receiver operating characteristic (ROC) curve and area under curve to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
  measure: Sensitivity and specificity
  time frame: 1 month
  description: We used sensitivity and specificity to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
  measure: Positive predictive value, negative predictive value
  time frame: 1 month
  description: We used positive predictive value and negative predictive value to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
  measure: F1 score
  time frame: 1 month
  description: We used F1 score to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
  arm group label: Retinal diseases diagnosed by artificial intelligence algorithm
  description: An artificial intelligence algorithm was applied to diagnose referral diabetes retinopathy, referral age-related macular degeneration, referral possible glaucoma, pathological myopia, retinal vein occlusion, macular hole, macular epiretinal membrane, hypertensive retinopathy, myelinated fibers, retinitis pigmentosa and other retinal lesions from fundus photography.
  intervention type: Diagnostic Test
  intervention name: artificial intelligence algorithm
  description: Retinal diseases diagnosed by artificial intelligence algorithm
  arm group label: Retinal diseases diagnosed by artificial intelligence algorithm
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 1 Year
  maximum age: 100 Years
  last name: Wenbin Wei, MD
  phone: 58269516
  email: weiwenbintr@163.cim
  last name: Ruiheng Zhang, MD
  phone: 18801121782
  email: zhangruihengsy@outlook.com
  facility: 
  status: Recruiting
  contact: 
  investigator: 
  country: China
  responsible party type: Principal Investigator
  investigator affiliation: Beijing Tongren Hospital
  investigator full name: Wenbin Wei
  investigator title: Prof
  mesh term: Retinal Diseases
  sharing ipd: No