<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/NCT04464252</url>
  </required_header>
  <id_info>
    <org_study_id>APHP200215</org_study_id>
    <nct_id>NCT04464252</nct_id>
  </id_info>
  <brief_title>Development and Validation of an Automated Three-dimensional Cephalometry Method</brief_title>
  <acronym>AutoCEPH-3D</acronym>
  <official_title>Development and Validation of an Automated Three-dimensional Cephalometry Method</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Assistance Publique - Hôpitaux de Paris</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Laboratoire de Biomécanique Georges Charpak</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Assistance Publique - Hôpitaux de Paris</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>
      This study is aimed to develop and assess the validity of an algorithm for automated&#xD;
      three-dimensional cephalometry.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Cephalometric analysis is a standardized diagnostic method used daily by orthodontists and&#xD;
      maxillofacial surgeons. It is based on linear and angular measurements performed on&#xD;
      radiographic images. This examination is traditionally done manually on two-dimensional&#xD;
      radiographs, which does not allow to analyze finely the bilateral structures which are found&#xD;
      superimposed. Cephalometric analysis of three-dimensional imaging (cone beam computed&#xD;
      tomography (CBCT) or computed tomography scan (CT-Scan)) may provide additional diagnostic&#xD;
      information, particularly for patients with maxillofacial abnormalities or marked&#xD;
      asymmetries.&#xD;
&#xD;
      One of the obstacle to the clinical use of three-dimensional cephalometric analysis is the&#xD;
      time and expertise needed to manually place the landmarks. Automatic methods described in&#xD;
      literature are preliminary and lack validation in a clinical context.&#xD;
&#xD;
      Our retrospective observational study is aimed to develop and validate a new automated&#xD;
      three-dimensional cephalometry method. This method will be based on a deep learning algorithm&#xD;
      trained from a database of pre-surgery CT-Scans of patients with have undergone an&#xD;
      orthognathic surgery. These CT-Scans will be manually annotated to provide a reference&#xD;
      standard for the training of the algorithm and its evaluation. The validation of our results&#xD;
      will focus on demonstrating the diagnostic effectiveness and robustness of three-dimensional&#xD;
      cephalometric measurements obtained in this clinical context.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">April 30, 2022</start_date>
  <completion_date type="Actual">April 30, 2022</completion_date>
  <primary_completion_date type="Actual">April 30, 2022</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>Retrospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Average distances (in mm) between the points placed automatically and the points placed manually (reference).</measure>
    <time_frame>through study completion, an average of 1 year</time_frame>
  </primary_outcome>
  <secondary_outcome>
    <measure>Percentage of points placed automatically within 2 / 3 / 4 mm of the the points placed manually (reference)</measure>
    <time_frame>through study completion, an average of 1 year</time_frame>
  </secondary_outcome>
  <enrollment type="Actual">453</enrollment>
  <condition>Orthognathic Surgery</condition>
  <condition>Dentofacial Deformities</condition>
  <eligibility>
    <study_pop>
      <textblock>
        Primary care hospital (hopital de la Pitié-Salpêtrière, AP-HP, Paris, France),&#xD;
        maxillofacial surgery department&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          1. Patients followed in the maxillofacial surgery department of Pitie-Salpetriere&#xD;
             hospital (AP-HP, Paris, France) since 2014;&#xD;
&#xD;
          2. who underwent orthognathic surgery;&#xD;
&#xD;
          3. who had, for surgery planning, a three-dimensional radiographic examination (CT-Scan)&#xD;
             which has been segmented for the manufacture of a custom-made device;&#xD;
&#xD;
          4. who did not object to the research.&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>15 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <location>
    <facility>
      <name>Service de Chirurgie Maxillo-Faciale, Service de Chirurgie Maxillo-Faciale</name>
      <address>
        <city>Paris</city>
        <zip>75013</zip>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>France</country>
  </location_countries>
  <verification_date>October 2022</verification_date>
  <study_first_submitted>June 17, 2020</study_first_submitted>
  <study_first_submitted_qc>July 8, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">July 9, 2020</study_first_posted>
  <last_update_submitted>October 28, 2022</last_update_submitted>
  <last_update_submitted_qc>October 28, 2022</last_update_submitted_qc>
  <last_update_posted type="Actual">October 31, 2022</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>Imaging, three-dimensional</keyword>
  <keyword>Cephalometry</keyword>
  <keyword>Anatomic Landmarks</keyword>
  <keyword>Radiographic Image Interpretation</keyword>
  <keyword>Deep Learning</keyword>
  <keyword>Orthodontics</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Dentofacial Deformities</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/NCT04464252
org study id: APHP200215
nct id: NCT04464252
lead sponsor: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
This study is aimed to develop and assess the validity of an algorithm for automated
      three-dimensional cephalometry.
Cephalometric analysis is a standardized diagnostic method used daily by orthodontists and
      maxillofacial surgeons. It is based on linear and angular measurements performed on
      radiographic images. This examination is traditionally done manually on two-dimensional
      radiographs, which does not allow to analyze finely the bilateral structures which are found
      superimposed. Cephalometric analysis of three-dimensional imaging (cone beam computed
      tomography (CBCT) or computed tomography scan (CT-Scan)) may provide additional diagnostic
      information, particularly for patients with maxillofacial abnormalities or marked
      asymmetries.

      One of the obstacle to the clinical use of three-dimensional cephalometric analysis is the
      time and expertise needed to manually place the landmarks. Automatic methods described in
      literature are preliminary and lack validation in a clinical context.

      Our retrospective observational study is aimed to develop and validate a new automated
      three-dimensional cephalometry method. This method will be based on a deep learning algorithm
      trained from a database of pre-surgery CT-Scans of patients with have undergone an
      orthognathic surgery. These CT-Scans will be manually annotated to provide a reference
      standard for the training of the algorithm and its evaluation. The validation of our results
      will focus on demonstrating the diagnostic effectiveness and robustness of three-dimensional
      cephalometric measurements obtained in this clinical context.
observational model: Cohort
time perspective: Retrospective
measure: Average distances (in mm) between the points placed automatically and the points placed manually (reference).
time frame: through study completion, an average of 1 year
measure: Percentage of points placed automatically within 2 / 3 / 4 mm of the the points placed manually (reference)
time frame: through study completion, an average of 1 year
study pop: 
sampling method: Probability Sample
criteria: 
gender: All
minimum age: 15 Years
maximum age: N/A
healthy volunteers: No
facility: 
country: France
responsible party type: Sponsor
mesh term: Dentofacial Deformities
sharing ipd: No