<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/NCT03766737</url>
  </required_header>
  <id_info>
    <org_study_id>CCPMOH2018-China-12</org_study_id>
    <nct_id>NCT03766737</nct_id>
  </id_info>
  <brief_title>Validation of the Utility of an Intelligent Visual Acuity Diagnostic System for Children</brief_title>
  <official_title>Validation of the Utility of an Intelligent Visual Acuity Diagnostic System for Children: Using a Human-in-the-loop Artificial Intelligence Paradigm</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>
    <has_dmc>Yes</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>
      Visual development during early childhood is a vital process. Examining the visual acuity of&#xD;
      children is essential for the early detection of visual abnormality, but performing such an&#xD;
      assessment in children is challenging. Here, the investigators developed a human-in-the-loop&#xD;
      artificial intelligence (AI) paradigm that combines traditional vision examination and AI&#xD;
      with integrated software and hardware, thus making the vision examination easy to perform.&#xD;
      The investigator also establish a entity intelligent visual acuity diagnostic system based on&#xD;
      the paradigm, and conduct clinical trial to validate if the diagnostic system can offsetting&#xD;
      the shortcomings of human doctors.&#xD;
    </textblock>
  </brief_summary>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">May 20, 2018</start_date>
  <completion_date type="Actual">July 20, 2018</completion_date>
  <primary_completion_date type="Actual">July 20, 2018</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>The proportion of accurate, mistaken and miss detection of the intelligent visual acuity diagnostic system.</measure>
    <time_frame>Up to 5 years</time_frame>
  </primary_outcome>
  <number_of_arms>1</number_of_arms>
  <enrollment type="Actual">50</enrollment>
  <condition>Ophthalmopathy</condition>
  <condition>Artificial Intelligence</condition>
  <arm_group>
    <arm_group_label>Eligible patients for AI test.</arm_group_label>
    <arm_group_type>Other</arm_group_type>
    <description>Device: An intelligent visual acuity diagnostic system for children. An artificial intelligence to evaluate children's vision.</description>
  </arm_group>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>An intelligent visual acuity diagnostic system for children</intervention_name>
    <description>An artificial intelligence to make evaluation and of children's vision.</description>
    <arm_group_label>Eligible patients for AI test.</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Paediatric patients from eye clinic written informed consents provided&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>1 Month</minimum_age>
    <maximum_age>14 Years</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Lin Haotian, M.D, Ph.D</last_name>
    <role>Principal Investigator</role>
    <affiliation>Zhongshan Ophthalmic Center, Sun Yat-sen University</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Zhongshan Ophthalmic Center, Sun Yat-sen University</name>
      <address>
        <city>Guangzhou</city>
        <state>Guangdong</state>
        <zip>510000</zip>
        <country>China</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>China</country>
  </location_countries>
  <verification_date>December 2018</verification_date>
  <study_first_submitted>December 5, 2018</study_first_submitted>
  <study_first_submitted_qc>December 5, 2018</study_first_submitted_qc>
  <study_first_posted type="Actual">December 6, 2018</study_first_posted>
  <last_update_submitted>December 5, 2018</last_update_submitted>
  <last_update_submitted_qc>December 5, 2018</last_update_submitted_qc>
  <last_update_posted type="Actual">December 6, 2018</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Sun Yat-sen University</investigator_affiliation>
    <investigator_full_name>Haotian Lin</investigator_full_name>
    <investigator_title>Clinical Professor</investigator_title>
  </responsible_party>
  <keyword>Vision Disorders</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Eye Diseases</mesh_term>
  </condition_browse>
  <!-- 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/NCT03766737
org study id: CCPMOH2018-China-12
nct id: NCT03766737
lead sponsor: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
Visual development during early childhood is a vital process. Examining the visual acuity of
      children is essential for the early detection of visual abnormality, but performing such an
      assessment in children is challenging. Here, the investigators developed a human-in-the-loop
      artificial intelligence (AI) paradigm that combines traditional vision examination and AI
      with integrated software and hardware, thus making the vision examination easy to perform.
      The investigator also establish a entity intelligent visual acuity diagnostic system based on
      the paradigm, and conduct clinical trial to validate if the diagnostic system can offsetting
      the shortcomings of human doctors.
allocation: N/A
intervention model: Single Group Assignment
primary purpose: Diagnostic
masking: None (Open Label)
measure: The proportion of accurate, mistaken and miss detection of the intelligent visual acuity diagnostic system.
time frame: Up to 5 years
arm group label: Eligible patients for AI test.
arm group type: Other
description: Device: An intelligent visual acuity diagnostic system for children. An artificial intelligence to evaluate children's vision.
intervention type: Device
intervention name: An intelligent visual acuity diagnostic system for children
description: An artificial intelligence to make evaluation and of children's vision.
arm group label: Eligible patients for AI test.
criteria: 
gender: All
minimum age: 1 Month
maximum age: 14 Years
healthy volunteers: Accepts Healthy Volunteers
last name: Lin Haotian, M.D, Ph.D
role: Principal Investigator
affiliation: Zhongshan Ophthalmic Center, Sun Yat-sen University
facility: 
country: China
responsible party type: Principal Investigator
investigator affiliation: Sun Yat-sen University
investigator full name: Haotian Lin
investigator title: Clinical Professor
mesh term: Eye Diseases