<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/NCT03773458</url>
  </required_header>
  <id_info>
    <org_study_id>CCPMOH2018-China-13</org_study_id>
    <nct_id>NCT03773458</nct_id>
  </id_info>
  <brief_title>Validation of the Utility of an Artificial System for the Large-scale Screening of Scoliosis</brief_title>
  <official_title>Validation of the Utility of an Artificial System for the Large-scale Screening of Scoliosis Using Back Images</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>
      Traditional school scoliosis screening approaches remains debatable due to unnecessary&#xD;
      referal and excessive cost. Deep learning algorithms have proven to be powerful tools for the&#xD;
      detection of multiple diseases; however, the application of such methods in scoliosis&#xD;
      screening requires further assessment and validation. Here, the investigators develop an&#xD;
      artificial system for the automated screening of scoliosis using disrobed back images, and&#xD;
      conduct clinical trial to validate if the diagnostic system can offsetting the shortcomings&#xD;
      of human doctors.&#xD;
    </textblock>
  </brief_summary>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">June 1, 2018</start_date>
  <completion_date type="Actual">July 30, 2018</completion_date>
  <primary_completion_date type="Actual">July 30, 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">500</enrollment>
  <condition>Orthopedic Disorder of Spine</condition>
  <condition>Artificial Intelligence</condition>
  <condition>Scoliosis</condition>
  <arm_group>
    <arm_group_label>Eligible patients for AI test.</arm_group_label>
    <arm_group_type>Other</arm_group_type>
    <description>Device: An artificial system for the screening of scoliosis</description>
  </arm_group>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>An artificial system for the screening of scoliosis</intervention_name>
    <description>An artificial intelligence to make evaluation of scoliosis using back images</description>
    <arm_group_label>Eligible patients for AI test.</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  1.Patients included both pretreatment back photos and whole spine (C7-S1) standing&#xD;
             X-ray or ultrasound images (for healthy population); 2. All the documents are clear to&#xD;
             be recognized by naked eyes; 3. Back photos and are taken at the same time (not&#xD;
             &gt;1month); 4.Patients were consider as idiopathic scoliosis according to clinical&#xD;
             photos.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  1. Patients were considered as non-idiopathic scoliosis for obvious abnormal features&#xD;
             of trunck,such as Cafe-au-Lait spots for neurofibromatosis, Spider finger, Abnormal&#xD;
             hair spot of back, pelvic tilt, lower limb discrepancy and so on; 2.The taken time&#xD;
             between back photo and X-ray or ultrasound was more than 1month; 3.The clinical photos&#xD;
             and images were not clear; 4. The X-ray film or ultrasound images not including whole&#xD;
             spine (C7-S1).&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>10 Years</minimum_age>
    <maximum_age>22 Years</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Haotian Lin</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 10, 2018</study_first_submitted>
  <study_first_submitted_qc>December 10, 2018</study_first_submitted_qc>
  <study_first_posted type="Actual">December 12, 2018</study_first_posted>
  <last_update_submitted>December 10, 2018</last_update_submitted>
  <last_update_submitted_qc>December 10, 2018</last_update_submitted_qc>
  <last_update_posted type="Actual">December 12, 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>Orthopedic Disorder of Spine</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Scoliosis</mesh_term>
    <mesh_term>Musculoskeletal Diseases</mesh_term>
    <mesh_term>Spinal 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/NCT03773458
org study id: CCPMOH2018-China-13
nct id: NCT03773458
lead sponsor: 
is fda regulated drug: No
is fda regulated device: No
Traditional school scoliosis screening approaches remains debatable due to unnecessary
      referal and excessive cost. Deep learning algorithms have proven to be powerful tools for the
      detection of multiple diseases; however, the application of such methods in scoliosis
      screening requires further assessment and validation. Here, the investigators develop an
      artificial system for the automated screening of scoliosis using disrobed back images, 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 artificial system for the screening of scoliosis
intervention type: Device
intervention name: An artificial system for the screening of scoliosis
description: An artificial intelligence to make evaluation of scoliosis using back images
arm group label: Eligible patients for AI test.
criteria: 
gender: All
minimum age: 10 Years
maximum age: 22 Years
healthy volunteers: Accepts Healthy Volunteers
last name: Haotian Lin
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: Scoliosis
mesh term: Musculoskeletal Diseases
mesh term: Spinal Diseases