<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/NCT03790930</url>
  </required_header>
  <id_info>
    <org_study_id>SHSY180624</org_study_id>
    <nct_id>NCT03790930</nct_id>
  </id_info>
  <brief_title>Deep-learning Based Classification of Spine CT</brief_title>
  <acronym>DETECT</acronym>
  <official_title>Deep-learning Based Classification of Spine CT</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Shanghai 10th People's Hospital</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Third Affiliated Hospital, Sun Yat-Sen University</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Shanghai 10th People's 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>
      It is time-consuming for spine surgeons or radiologists to conduct manual classifications of&#xD;
      spinal CT, which may also be correlated with high inter-observer variance. With the&#xD;
      development of computer science, deep learning has emerged as a promising technique to&#xD;
      classify images from individual level to pixel level. The main of the study is to&#xD;
      automatically identify and classify the lesions, or segment targeted structures on spinal CT&#xD;
      with deep learning.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Computer tomography (CT) is one of the most important imaging tool to assist the diagnostic&#xD;
      and treatment of spinal disease. Classification of specific targets (e.g. individuals,&#xD;
      lesions, etc.) is one of the most common mission of medical image analysis. However, it is&#xD;
      time-consuming for spine surgeons or radiologists to conduct manual classifications of spinal&#xD;
      CT, which may also be correlated with high inter-observer variance. With the development of&#xD;
      computer science, deep learning has emerged as a promising technique to classify images from&#xD;
      individual level to pixel level. The main of the study is to automatically identify and&#xD;
      classify the lesions, or segment targeted structures on spinal CT with deep learning.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Recruiting</last_known_status>
  <start_date type="Actual">February 22, 2019</start_date>
  <completion_date type="Anticipated">May 2020</completion_date>
  <primary_completion_date type="Anticipated">May 2020</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Case-Only</observational_model>
    <time_perspective>Retrospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>classification accuracy</measure>
    <time_frame>1 day</time_frame>
    <description>classification accuracy (e.g. area under the curve, etc.)</description>
  </primary_outcome>
  <primary_outcome>
    <measure>segmentation accuracy</measure>
    <time_frame>1 day</time_frame>
    <description>segmentation accuracy of multiple structures (e.g. Dice score, etc.)</description>
  </primary_outcome>
  <number_of_groups>1</number_of_groups>
  <enrollment type="Anticipated">500</enrollment>
  <condition>Surgical Procedure, Unspecified</condition>
  <arm_group>
    <arm_group_label>thin layer CT</arm_group_label>
    <description>Thin-layer CT will be manually labeled and used to train, validate and test deep learning algorithm.</description>
  </arm_group>
  <intervention>
    <intervention_type>Diagnostic Test</intervention_type>
    <intervention_name>deep learning</intervention_name>
    <description>manually labeled samples will be used to train, validate and test deep learning algorithm, and then realize automatic classification.</description>
    <arm_group_label>thin layer CT</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        patients with thin layer spinal CT covering targeted level will be included.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
        - spinal thin layer CT&#xD;
&#xD;
        Exclusion Critera:&#xD;
&#xD;
          -  medals or other implants induce artifact&#xD;
&#xD;
          -  poor image quality&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>65 Years</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Shisheng He, M.D.</last_name>
    <role>Principal Investigator</role>
    <affiliation>Shanghai 10th People's Hospital</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Guoxin Fan</last_name>
    <phone>008602166307580</phone>
    <email>gfan@tongji.edu.cn</email>
  </overall_contact>
  <location>
    <facility>
      <name>Shanghai Tenth People's Hospital</name>
      <address>
        <city>Shanghai</city>
        <state>Shanghai</state>
        <zip>200072</zip>
        <country>China</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Guoxin Fan</last_name>
      <email>1610707@tongji.edu.cn</email>
    </contact>
  </location>
  <location_countries>
    <country>China</country>
  </location_countries>
  <verification_date>May 2020</verification_date>
  <study_first_submitted>November 16, 2018</study_first_submitted>
  <study_first_submitted_qc>December 29, 2018</study_first_submitted_qc>
  <study_first_posted type="Actual">January 2, 2019</study_first_posted>
  <last_update_submitted>May 10, 2020</last_update_submitted>
  <last_update_submitted_qc>May 10, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">May 12, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Shanghai 10th People's Hospital</investigator_affiliation>
    <investigator_full_name>Shisheng He, MD</investigator_full_name>
    <investigator_title>Executive Director of Orthopedic Department</investigator_title>
  </responsible_party>
  <patient_data>
    <sharing_ipd>Undecided</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/NCT03790930
org study id: SHSY180624
nct id: NCT03790930
lead sponsor: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
It is time-consuming for spine surgeons or radiologists to conduct manual classifications of
      spinal CT, which may also be correlated with high inter-observer variance. With the
      development of computer science, deep learning has emerged as a promising technique to
      classify images from individual level to pixel level. The main of the study is to
      automatically identify and classify the lesions, or segment targeted structures on spinal CT
      with deep learning.
Computer tomography (CT) is one of the most important imaging tool to assist the diagnostic
      and treatment of spinal disease. Classification of specific targets (e.g. individuals,
      lesions, etc.) is one of the most common mission of medical image analysis. However, it is
      time-consuming for spine surgeons or radiologists to conduct manual classifications of spinal
      CT, which may also be correlated with high inter-observer variance. With the development of
      computer science, deep learning has emerged as a promising technique to classify images from
      individual level to pixel level. The main of the study is to automatically identify and
      classify the lesions, or segment targeted structures on spinal CT with deep learning.
observational model: Case-Only
time perspective: Retrospective
measure: classification accuracy
time frame: 1 day
description: classification accuracy (e.g. area under the curve, etc.)
measure: segmentation accuracy
time frame: 1 day
description: segmentation accuracy of multiple structures (e.g. Dice score, etc.)
arm group label: thin layer CT
description: Thin-layer CT will be manually labeled and used to train, validate and test deep learning algorithm.
intervention type: Diagnostic Test
intervention name: deep learning
description: manually labeled samples will be used to train, validate and test deep learning algorithm, and then realize automatic classification.
arm group label: thin layer CT
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: 65 Years
healthy volunteers: No
last name: Shisheng He, M.D.
role: Principal Investigator
affiliation: Shanghai 10th People's Hospital
last name: Guoxin Fan
phone: 008602166307580
email: gfan@tongji.edu.cn
facility: 
status: Recruiting
contact: 
country: China
responsible party type: Principal Investigator
investigator affiliation: Shanghai 10th People's Hospital
investigator full name: Shisheng He, MD
investigator title: Executive Director of Orthopedic Department
sharing ipd: Undecided