<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/NCT03759483</url>
  </required_header>
  <id_info>
    <org_study_id>2018KYPJ125</org_study_id>
    <nct_id>NCT03759483</nct_id>
  </id_info>
  <brief_title>Diagnostic Efficacy of CNN in Differentiation of Visual Field</brief_title>
  <official_title>Diagnostic Efficacy of Convolutional Neural Network Based Algorithm in Differentiation of Glaucomatous Visual Field From Non-glaucomatous Visual Field</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Sun Yat-sen University</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Sun Yat-sen University</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>
      Glaucoma is currently the leading cause of irreversible blindness in the world. The&#xD;
      multi-center study is designed to evaluate the efficacy of the convolutional neural network&#xD;
      based algorithm in differentiation of glaucomatous from non-glaucomatous visual field, and to&#xD;
      assess its utility in the real world.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Glaucoma is the world's leading cause of irreversible blind, characterized by progressive&#xD;
      retinal nerve fiber layer thinning and visual field defects. Visual field test is one of the&#xD;
      gold standards for diagnosis and evaluation of progression of glaucoma. However, there is no&#xD;
      universally accepted standard for the interpretation of visual field results, which is&#xD;
      subjective and requires a large amount of experience. At present, artificial intelligence has&#xD;
      achieved the accuracy comparable to human physicians in the interpretation of medical imaging&#xD;
      of many different diseases. Previously, we have trained a deep convolutional neural network&#xD;
      to read the visual field reports, which has even higher diagnostic efficacy than&#xD;
      ophthalmologists. The current multi-center study is designed to evaluate the efficacy of the&#xD;
      convolutional neural network based algorithm in differentiation of glaucomatous from&#xD;
      non-glaucomatous visual field, compare its performance with ophthalmologists and to assess&#xD;
      its utility in the real world.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">March 15, 2019</start_date>
  <completion_date type="Actual">December 31, 2019</completion_date>
  <primary_completion_date type="Actual">December 31, 2019</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Other</observational_model>
    <time_perspective>Other</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>AUC value of convolutional neural network in differentiation of Glaucoma visual field from non-glaucoma visual field</measure>
    <time_frame>from Jan 2019 to Jan 2020</time_frame>
  </primary_outcome>
  <secondary_outcome>
    <measure>Sensitivity and specificity of convolutional neural network in detection of glaucoma visual field</measure>
    <time_frame>from Jan 2019 to Jan 2020</time_frame>
  </secondary_outcome>
  <number_of_groups>2</number_of_groups>
  <enrollment type="Actual">437</enrollment>
  <condition>Diagnositic Efficacy of Deep Convolutional Neural Network in Differentiation of Glaucoma Visual Field From Non-glaucoma Visual Field</condition>
  <arm_group>
    <arm_group_label>AI group</arm_group_label>
    <description>The visual field reports in this group will be evaluated by the convolutional neural network.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Human group</arm_group_label>
    <description>The visual field reports in this group will be evaluated by 3 ophthalmologists independently.</description>
  </arm_group>
  <intervention>
    <intervention_type>Diagnostic Test</intervention_type>
    <intervention_name>AI diagnostic algorithm</intervention_name>
    <description>The visual fields collected would be assessed by the algorithm and ophthalmologists independently. The performance of the algorithm and the ophthalmologists would be compared, including accuracy, AUC, sensitivity and specificity.</description>
    <arm_group_label>AI group</arm_group_label>
    <other_name>Standard diagnostic procedure</other_name>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Patients from clinics in different eye centers across China. Each subject must be diagnosed&#xD;
        based on comprehensive medical tests and medical records. The leading center will read all&#xD;
        the medical data to give out diagnosis as the gold standard.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          1. Age≥18;&#xD;
&#xD;
          2. Informed consent obtained;&#xD;
&#xD;
          3. Diagnosed with specific ocular diseases;&#xD;
&#xD;
          4. Able to perform visual field test&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
        Incomplete clinical data to support diagnosis&#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>
  <location>
    <facility>
      <name>Zhongshan Ophthalmic Center</name>
      <address>
        <city>Guangzhou</city>
        <state>Guangdong</state>
        <zip>51000</zip>
        <country>China</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>China</country>
  </location_countries>
  <verification_date>January 2020</verification_date>
  <study_first_submitted>November 26, 2018</study_first_submitted>
  <study_first_submitted_qc>November 28, 2018</study_first_submitted_qc>
  <study_first_posted type="Actual">November 30, 2018</study_first_posted>
  <last_update_submitted>January 23, 2020</last_update_submitted>
  <last_update_submitted_qc>January 23, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">January 27, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Sun Yat-sen University</investigator_affiliation>
    <investigator_full_name>Xiulan Zhang</investigator_full_name>
    <investigator_title>Director of Clinical Research Center，Director of Institution of Drug Clinical Trials</investigator_title>
  </responsible_party>
  <keyword>Deep convolutional neural network</keyword>
  <keyword>Visual field</keyword>
  <keyword>Glaucoma</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Glaucoma</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/NCT03759483
org study id: 2018KYPJ125
nct id: NCT03759483
lead sponsor: 
is fda regulated drug: No
is fda regulated device: No
Glaucoma is currently the leading cause of irreversible blindness in the world. The
      multi-center study is designed to evaluate the efficacy of the convolutional neural network
      based algorithm in differentiation of glaucomatous from non-glaucomatous visual field, and to
      assess its utility in the real world.
Glaucoma is the world's leading cause of irreversible blind, characterized by progressive
      retinal nerve fiber layer thinning and visual field defects. Visual field test is one of the
      gold standards for diagnosis and evaluation of progression of glaucoma. However, there is no
      universally accepted standard for the interpretation of visual field results, which is
      subjective and requires a large amount of experience. At present, artificial intelligence has
      achieved the accuracy comparable to human physicians in the interpretation of medical imaging
      of many different diseases. Previously, we have trained a deep convolutional neural network
      to read the visual field reports, which has even higher diagnostic efficacy than
      ophthalmologists. The current multi-center study is designed to evaluate the efficacy of the
      convolutional neural network based algorithm in differentiation of glaucomatous from
      non-glaucomatous visual field, compare its performance with ophthalmologists and to assess
      its utility in the real world.
observational model: Other
time perspective: Other
measure: AUC value of convolutional neural network in differentiation of Glaucoma visual field from non-glaucoma visual field
time frame: from Jan 2019 to Jan 2020
measure: Sensitivity and specificity of convolutional neural network in detection of glaucoma visual field
time frame: from Jan 2019 to Jan 2020
arm group label: AI group
description: The visual field reports in this group will be evaluated by the convolutional neural network.
arm group label: Human group
description: The visual field reports in this group will be evaluated by 3 ophthalmologists independently.
intervention type: Diagnostic Test
intervention name: AI diagnostic algorithm
description: The visual fields collected would be assessed by the algorithm and ophthalmologists independently. The performance of the algorithm and the ophthalmologists would be compared, including accuracy, AUC, sensitivity and specificity.
arm group label: AI group
other name: Standard diagnostic procedure
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: Accepts Healthy Volunteers
facility: 
country: China
responsible party type: Principal Investigator
investigator affiliation: Sun Yat-sen University
investigator full name: Xiulan Zhang
investigator title: Director of Clinical Research Center，Director of Institution of Drug Clinical Trials
mesh term: Glaucoma
sharing ipd: No